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14-06-2009, 09:16 AM
Post: #1
FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
A SEMINAR REPORT ON
FACE RECOGNITION TECHNOLOGY


Submitted by:AJIT KUMAR ASHWANI
COMPUTER SCIENCE & ENGINEERING
SCHOOL OF ENGINEERING
COCHIN UNIVERSITY OF SCIENCE &TECHNOLOGY,
KOCHI “ 682022

ABSTRACT Wouldnâ„¢t you love to replace password based access control to avoid having to reset forgotten password and worry about the integrity of your system? Wouldnâ„¢t you like to rest secure in comfort that your healthcare system does not merely on your social security number as proof of your identity for granting access to your medical records? Because each of these questions is becoming more and more important, access to a reliable personal identification is becoming increasingly essential .Conventional method of identification based on possession of ID cards or exclusive knowledge like a social security number or a password are not all together reliable. ID cards can be lost forged or misplaced; passwords can be forgotten or compromised. But a face is undeniably connected to its owner. It cannot be borrowed stolen or easily forged. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Itâ„¢s nontransferable. The system can then compare scans to records stored in a central or local database or even on a smart card.


Face Recognition Technology
1. INTRODUCTION The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database or even on a smart card.
1.1 What are biometrics?
A biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individualâ„¢s identity. Biometrics can measure both physiological and behavioral characteristics. Physiological biometrics (based on measurements and data derived from direct measurement of a part of the human body) include:
a. Finger-scan
b. Facial Recognition
c. Iris-scan
d. Retina-scan
e. Hand-scan
Behavioral biometrics (based on measurements and data derived from an action) include:
a. Voice-scan
b. Signature-scan
c. Keystroke-scan
A biometric system refers to the integrated hardware and software used to conduct biometric
identification or verification.
1.2 Why we choose face recognition over other biometric? There are number reasons to choose face recognition. This includes the following
a. It requires no physical interaction on behalf of the user.
b. It is accurate and allows for high enrolment and verification rates.
c. It does not require an expert to interpret the comparison result.
d. It can use your existing hardware infrastructure, existing camaras and image capture Devices will work with no problems
e. It is the only biometric that allow you to perform passive identification in a one to.
Many environments (e.g.: identifying a terrorist in a busy Airport terminal 11

2. FACE RECOGNITION
THE FACE:
The face is an important part of who you are and how people identify you. Except in the case of identical twins, the face is arguably a person's most unique physical characteristics. While humans have the innate ability to recognize and distinguish different faces for millions of years, computers are just now catching up. For face recognition there are two types of comparisons .the first is verification. This is where the system compares the given individual with who that individual says they are and gives a yes or no decision. The second is identification. This is where the system compares the given individual to all the Other individuals in the database and gives a ranked list of matches. All identification or authentication technologies operate using the following four stages:
a. Capture: A physical or behavioural sample is captured by the system during
Enrollment and also in identification or verification process
b. Extraction: unique data is extracted from the sample and a template is created.
c. Comparison: the template is then compared with a new sample.
d. Match/non match: the system decides if the features extracted from the new
Samples are a match or a non match Face recognition technology analyze the unique shape, pattern and positioning of the facial features. Face recognition is very complex technology and is largely software based. This Biometric Methodology establishes the analysis framework with tailored algorithms for each type of biometric device. Face recognition starts with a picture, attempting to find a person in the image. This can be accomplished using several methods including movement, skin tones, or blurred human shapes. The face recognition system locates the head and finally the eyes of the individual. A matrix is then developed based on the characteristics of the Individualâ„¢s face. The method of defining the matrix varies according to the algorithm (the mathematical process used by the computer to perform the comparison). This matrix is then compared to matrices that are in a database and a similarity score is generated for each comparison. Artificial intelligence is used to simulate human interpretation of faces. In order to increase the accuracy and adaptability, some kind of machine learning has to be implemented. There are essentially two methods of capture. One is video imaging and the other is thermal imaging. Video imaging is more common as standard video cameras can be used. The precise position and the angle of the head and the surrounding lighting conditions may affect the system performance. The complete facial image is usually captured and a number of points on the face can then be mapped, position of the eyes, mouth and the nostrils as a example. More advanced technologies make 3-D map of the face which multiplies the possible measurements that can be made. Thermal imaging has better accuracy as it uses facial temperature variations caused by vein structure as the distinguishing traits. As the heat pattern is emitted from the face itself without source of external radiation these systems can capture images despite the lighting condition, even in the dark. The drawback is high cost. They are more expensive than standard video cameras.
3. CAPTURING OF IMAGE BY STANDARD VIDEO
CAMERAS

The image is optical in characteristics and may be thought of as a collection of a large number of bright and dark areas representing the picture details. At an instant there will be large number of picture details existing simultaneously each representing the level of brightness of the scene to be reproduced. In other words the picture information is a function of two variables: time and space. Therefore it would require infinite number of channels to transmit optical information corresponding to picture elements simultaneously. There is practical difficulty in transmitting all information simultaneously so we use a method called scanning. Here the conversion of optical information to electrical form and its transmission is carried out element by element one at a time in a sequential manner to cover the entire image. A TV camera converts optical information into electrical information, the amplitude of which varies in accordance with variation of brightness. An optical image of the scene to be transmitted is focused by lense assembly on the rectangular glass plate of the camera tube. The inner side of this has a transparent coating on which is laid a very thin layer of photoconductive material. The photolayer has very high resistance when no light is falling on it but decreases depending on the intensity of light falling on it. An electron beam is formed by an electron gun in the TV camera tube. This beam is used to pick up the picture information now avilable on the target plate of varying resistace at each point. The electron beam is deflected by a pair of deflecting coils mounted on the glass envelope and kept mutually perpendicular to each other to achive scanning of the entire target area. The deflecting coils are fed seperately from two sweep oscillators, each operating at different frequencies. The magnetic deflection caused by current in one coil gives horizontal motion to the beam from left to right at a uniform rate and brings it back to the left side to commence the trace of the next line. The other coil is used to deflect the beam from top to bottom.
As the beam moves from element to element it encounters different resistance across the target plate depending on the resistance of the photoconductive coating. The result is flow of current which varies in magnitude as elements are scanned. The current passes through the load resistance Rl connected to conductive coating on one side of the DC supply source on the other. Depending on the magnitude of current a varying voltage appears across the resistance Rl and this corresponds to the optical information of the picture
4. COMPONENTS OF FACE RECOGNITION SYSTEMS

a. An automated mechanism that scans and captures a digital or an analog image of a living personal characteristics.(enrollment module)
b. Another entity which handles compression, processing, storage and compression of the captured data with stored data (database)
c. The third interfaces with the application system ( identification module) User interface captures the analog or digital image of the person's face. In the enrollment module the obtained sample is preprocessed and analyzed. This analyzed data is stored in the database for the purpose of future comparison. The database compresses the obtained sample and stores it. It should have retrival property also that is it compares all the stored sample with the newly obtained sample and retrives the matched sample for the purpose of verification by the user and determine whether the match declared is right or wrong. The verification module also consists of a preprocessing system. Verification means the system checks as to who the person says he or she is and gives a yes or no decision. In this module the newly obtained sample is preprocessed and compared with the sample stored in the database. The decision is taken depending on the match obtained from the database. Correspondingly the sample is accepted or rejected. Instead of verification module we can make use of identification module. In this the sample is compared with all the other samples stored in the database. For each comparison made a match score is given. The decision to accept or reject the sample depends on this match score falling above or below a predetermined threshold.
5. PERFORMANCE
False acceptance rate (FAR)
The probability that a system will incorrectly identify an individual or will fail
to reject an imposter. It is also called as type 2 error rate
FAR= NFA/NIIA
Where FAR= false acceptance rate
NFA= number of false acceptance
NIIA= number of imposter identification attempts
¢
False rejection rates (FRR)
The probability that a system will fail to identify an enrollee. It is also called
type 1 error rate.
FRR= NFR/NEIA
Where FRR= false rejection rates
NFR= number of false rejection rates
NEIA= number of enrollee identification attempt
Response time:
The time period required by a biometric system to return a decision on
identification of a sample.

Threshold/ decision Threshold:
The acceptance or rejection of a data is dependent on the match score falling above or below the threshold. The threshold is adjustable so that the system can be made more or less strict; depending on the requirements of any given application.
Enrollment time:
The time period a person must spend to have his/her facial reference template
successfully created.
Equal error rate:
When the decision threshold of a system is set so that the proportion of false rejection will be approximately equal to the proportion of false acceptance. This synonym is 'crossover rate'. The facial verification process involves computing the distance between the stored pattern and the live sample. The decision to accept or reject is dependent on a predetermined threshold. (Decision threshold).
6. IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
The implementation of face recognition technology includes the following four stages:
• Data acquisition
• Input processing
• Face image classification and decision making
6.1 Data acquisition:
The input can be recorded video of the speaker or a still image. A sample of 1 sec duration consists of a 25 frame video sequence. More than one camera can be used to produce a 3D representation of the face and to protect against the usage of photographs to gain unauthorized access.
6.2 Input processing:
A pre-processing module locates the eye position and takes care of the surrounding lighting condition and colour variance. First the presence of faces or face in a scene must be detected. Once the face is detected, it must be localized and Normalization process may be required to bring the dimensions of the live facial sample in alignment with the one on the template. Some facial recognition approaches use the whole face while others concentrate on facial components and/ or regions (such as lips, eyes etc). The appearance of the face can change considerably during speech and due to facial expressions. In particular the mouth is subjected to fundamental changes but is also very important source for discriminating faces. So an approach to personâ„¢s recognition is developed based on patio- temporal modeling of features extracted from talking face. Models are trained specific to a personâ„¢s speech articulate and the way that the person speaks. Person identification is performed by tracking mouth movements of the talking face and by estimating the likelyhood of each model of having generated the observed sequence of features. The model with the highest likelyhood is chosen as the recognized person. Synergetic computer are used to classify optical and audio features, respectively. A synergetic computer is a set of algorithm that simulate synergetic phenomena. In training phase the BIOID creates a prototype called faceprint for each person. A newly recorded pattern is preprocessed and compared with each faceprint stored in the database. As comparisons are made, the system assigns a value to the comparison using a scale of one to ten. If a score is above a predetermined threshold, a match is declared. .
From the image of the face, a particular trait is extracted. It may measure various nodal points of the face like the distance between the eyes ,width of nose etc. it is fed to a synergetic computer which consists of algorithm to capture, process, compare the sample with the one stored in the database. We can also track the lip movement which is also fed to the synergetic computer. Observing the likelyhood each of the samples with the one stored in the database we can accept or reject the sample.
7. HOW FACE RECOGNITION SYSTEMS WORK
An example Visionics, company based in a New Jersey is one of the many developers of facial recognition technology. The twist to its particular software, Face it is that it can pick someone's face from the rest of the scene and compare it to a database full of stored images. In order for this software to work, it has to know what a basic face looks like. Facial recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If you look at the mirror, you can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features. Visionics defines these landmarks as nodal points. There are about 80 nodal points on a human face. Here are few nodal points that are measured by the software.
• distance between the eyes
• width of the nose
• depth of the eye socket
• cheekbones
• jaw line
• chin

These nodal points are measured to create a numerical code, a string of numbers that represents a face in the database. This code is called faceprint. Only 14 to 22 nodal points are needed for faceit software to complete the recognition process
8. THE SOFTWARE
Facial recognition software falls into a larger group of technologies known as biometrics. Facial recognition methods may vary, but they generally involve a series of steps that serve to capture, analyze and compare your face to a database of stored images. Here is the basic process that is used by the Faceit system to capture and compare images:
8.1 Detection
When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. (An algorithm is a program that provides a set of instructions to accomplish a specific task). The system switches to a high-resolution search only after a head-like shape is detected.
8.2 Alignment
Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it.
8.3 Normalization
The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.
8.4 Representation
The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
8.5 Matching
The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation. The heart of the FaceIt facial recognition system is the Local Feature Analysis (LFA) algorithm. This is the mathematical technique the system uses to encode faces. The system maps the face and creates a faceprint, a unique numerical code for that face. Once the system has stored a faceprint, it can compare it to the thousands or millions of faceprints stored in a database. Each faceprint is stored as an 84-byte file. Using facial recognition software, police can zoom in with cameras and take a snapshot of a face. The system can match multiple faceprints at a rate of 60 million per minute from memory or 15 million per minute from hard disk. As comparisons are made, the system assigns a value to the comparison using a scale of one to 10. If a score is above a predetermined threshold, a match is declared. The operator then views the two photos that have been declared a match to be certain that the computer is accurate.
9. ADVANTAGES AND DISADVANTAGES
9.1 Advantages:
a. There are many benefits to face recognition systems such as its convinence and
Social acceptability.all you need is your picturetaken for it to work.
b. Face recognition is easy to use and in many cases it can be performed without a
Person even knowing.
c. Face recognition is also one of the most inexpensive biometric in the market and
Its price should continue to go down.
9.2 Disadvantage:
a. Face recognition systems canâ„¢t tell the difference between identical twins
10. APPLICATIONS
The natural use of face recognition technology is the replacement of PIN, physical tokens or both needed in automatic authorization or identification schemes. Additional uses are automation of human identification or role authentication in such cases where assistance of another human needed in verifying the ID cards and its beholder.
There are numerous applications for face recognition technology:
10.1 Government Use a. Law Enforcement: Minimizing victim trauma by narrowing mugshot searches, verifying
Identify for court records, and comparing school surveillance camera images to know child molesters.
b. Security/Counterterrorism. Access control, comparing surveillance images to Know terrorist.
c. Immigration: Rapid progression through Customs.
10.2 Commercial Use
a. Day Care: Verify identity of individuals picking up the children.
b. Residential Security: Alert homeowners of approaching personnel
c. Voter verification: Where eligible politicians are required to verify their identity during a
voting process this is intended to stop Ëœproxyâ„¢ voting where the vote may not go as
expected.
d. Banking using ATM: The software is able to quickly verify a customerâ„¢s face.

e. Physical access control of buildings areas, doors, cars or net access.
11. CONCLUSION
Face recognition technologies have been associated generally with very costly top secure applications. Today the core technologies have evolved and the cost of equipments is going down dramatically due to the intergration and the increasing processing power. Certain applications of face recognition technology are now cost effective, reliable and highly accurate. As a result there are no technological or financial barriers for stepping from the pilot project to widespread deployment
REFERENCES
1. Electronics for You: - Part 1 April 2001 Part 2 May 2001
2. Electronics World: - December 2002
3. IEEE Intelligent Systems - May/June 2003
4. Modern Television Engineering- Galati R.R 5.www.facereg.com
6. http://www.Imagestechnology.com
7.www.iee.com

Please Use Search http://seminarprojects.com/search.php wisely To Get More Information About A Seminar Or Project Topic
29-06-2010, 12:01 AM
Post: #2
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
FACE RECOGNITION TECHNOLOGY
[/b]Abstract [b]
At present, there are many methods for frontal face view recognition. Singular value decomposition (SVD) is one of the most important and useful factorizations in linear algebra. It describes how SVD is applied to problems involving image processing€in particular, how SVD aids the calculation of so-called eigenfaces, which provide an efficient representation of facial images in face recognition. Although the eigenface technique was developed for ordinary grayscale images, the technique is not limited to these images. Imagine an image where the different shades of gray convey the physical three dimensional structure of a face. Although the eigenface technique can again be applied, the problem is finding the three-dimensional image in the first place. We therefore also show how SVD can be used to reconstruct three-dimensional objects from a two-dimensional video stream.


please read http://www.seminarprojectsThread-seminar-report-on-face-recognition-technology for getting presentation of the technology face RECOGNITION ,,,


hope you may find it help..

Please Use Search http://seminarprojects.com/search.php wisely To Get More Information About A Seminar Or Project Topic
27-07-2010, 12:26 PM
Post: #3
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT

FACE RECOGNITION TECHNOLOGY
19-10-2010, 11:04 AM
Post: #4
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT

.ppt  BIOMETRICS-facial.ppt (Size: 284 KB / Downloads: 878)
BIOMETRICS: FACIAL RECOGNITION TECHNOLOGY

By Matthew Willert
PUAD 620


WHAT IS BIOMETRICS?

BIOMETRICS – any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual.


TWO MAIN USES


1) IDENTFICATION
-figure out “Who is X?”
-accomplished by system performing a “one-to-many” search
2) VERIFICATION
-answer the question “Is this X?”
-accomplished by the system performing a “one-to-one” search
26-01-2011, 12:19 AM
Post: #5
Photo RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
hi,can u provide full description on face recognition technology
27-01-2011, 11:19 AM
Post: #6
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
to get information about the topic face recognition technology full report ppt, and related topics refer the link bellow

http://www.seminarprojectsThread-face-recognition-technology-a-seminar-report

http://www.seminarprojectsThread-face-recognition-technology-a-seminar-report?page=4

http://www.seminarprojectsThread-seminar-report-on-face-recognition-technology

http://www.seminarprojectsThread-FACE-RECOGNITION-TECHNOLOGY-SEMINAR-REPORT--28533

http://www.seminarprojectsThread-face-recognition-technology--27390

http://www.seminarprojectsThread-face-recognition-technology-a-seminar-report?page=5

http://www.seminarprojectsThread-face-recognition-technology-a-seminar-report?page=2

http://www.seminarprojectsThread-face-recognition-technology-a-seminar-report?page=3

http://www.seminarprojectsThread-facial-recognition-system

http://www.seminarprojectsThread-advances-in-the-face-detection-and-recognition-technologies

http://www.seminarprojectsThread-face-recognition-using-laplacian-faces--5867

http://www.seminarprojectsThread-biometric-face-recognition

http://www.seminarprojectsThread-face-recognition-using-neural-networks-download-seminar-report

http://www.seminarprojectsThread-face-recognition-using-the-techniques-base-on-principal-component-analysis-pca

12-02-2011, 01:32 AM
Post: #7
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
thank u so much..i was so worried for my seminar.but now i m so feel confidence after finding this seminar report........
07-03-2011, 04:35 PM
Post: #8
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT

.doc  hk.doc (Size: 1.11 MB / Downloads: 269)
INTRODUCTION
The information age is quickly revolutionizing the way
transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences.
Using the proper PIN gains access, but the user of the PIN is not
verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many
people continue to choose easily guessed PIN's and passwords: birthdays,phone numbers and social security numbers. Recent cases of identity theft have
hightened the nee for methods to prove that someone is truly who he/she
claims to be. Face recognition technology may solve this problem since a face
is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database or even on a smart card.
What are biometrics?
A biometric is a unique, measurable characteristic of a human
being that can be used to automatically recognize an individual or verify an
individual’s identity. Biometrics can measure both physiological and
behavioral characteristics. Physiological biometrics (based on measurements
and data derived from direct measurement of a part of the human body)
include:
• Finger-scan
• Facial Recognition
• Iris-scan
• Retina-scan
• Hand-scan
Behavioral biometrics (based on measurements and data derived from an
action) include:
• Voice-scan
• Signature-scan
• Keystroke-scan
A “biometric system” refers to the integrated hardware and software used to conduct biometric identification or verification.
Why we choose face recognition over other biometric?
There are a number reasons to choose face recognition. This
includes the following
1. It requires no physical inetraction on behalf of the user.
2. It is accurate and allows for high enrolment and verification rates.
3. It does not require an expert to interpret the comparison result.
4. It can use your existing hardware infrastructure, existing camaras and
image capture devices will work with no problems.
5. It is the only biometric that allow you to perform passive identification in a one to many environment (eg: identifying a terrorist in a busy Airport
terminal.
FACE RECOGNITION
THE FACE:

The face is an important part of who you are and how people
identify you. Except in the case of identical twins, the face is arguably a
person's most unique physical characteristics. While humans have the innate
ability to recognize and distinguish different faces for millions of years ,
computers are just now catching up.
For face recognition there are two types of comparisons .the first
is verification. This is where the system compares the given individual with
who that individual says they are and gives a yes or no decision. The second is
identification. This is where the system compares the given individual to all the other individuals in the database and gives a ranked list of matches. All
identification or authentication technologies operate using the following four
stages:
• capture: a physical or behavioural sample is captured by the system
during enrollment and also in identification or verification process.
• Extraction: unique data is extracted from the sample and a template is
created.
• Comparison: the template is then compared with a new sample.
• Match/non match : the system decides if the features extracted from the
new sample are a match or a non match.
Face recognition technology analyze the unique shape ,pattern and
positioning of the facial features. Face recognition is very complex technology
and is largely software based. This Biometric Methodology establishes the
analysis framework with tailored algorithms for each type of biometric device.
Face recognition starts with a picture, attempting to find a person in the image.
This can be accomplished using several methods including movement, skin
tones, or blurred human shapes. The face recognition system locates the head
and finally the eyes of the individual. A matrix is then developed based on the
characteristics of the individual’s face. The method of defining the matrix
varies according to the algorithm (the mathematical process used by the
computer to perform the comparison). This matrix is then compared to
matrices that are in a database and a similarity score is generated for each
comparison.
Artificial intelligence is used to simulate human interpretation of
faces. In order to increase the accuracy and adaptability , some kind of
machine learning has to be implemented.
There are essentially two methods of capture. One is video
imaging and the other is thermal imaging. Video imaging is more common as
standard video cameras can be used. The precise position and the angle of the
head and the surrounding lighting conditions may affect the system
performance. The complete facial image is usually captured and a number of
points on the face can then be mapped, position of the eyes, mouth and the
nostrils as a example. More advanced technologies make 3-D map of the face
which multiplies the possible measurements that can be made. Thermal
imaging has better accuracy as it uses facial temperature variations caused by
vein structure as the distinguishing traits. As the heat pattern is emitted from
the face itself without source of external radiation these systems can capture
images despite the lighting condition, even in the dark. The drawback is high
cost. They are more expensive than standard video cameras.
12-03-2011, 12:00 PM
Post: #9
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
can u please send me ppt of face recognition technology
22-03-2011, 04:32 PM
Post: #10
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT

.doc  Face recognition.doc (Size: 279 KB / Downloads: 332)
CATEGORY- IMAGE PROCESSING
ABSTRACT:

Government agencies are investing a considerable amount of resources into improving security systems as result of recent terrorist events that dangerously exposed flaws and weaknesses in today’s safety mechanisms. Badge or password-based authentication procedures are too easy to hack. Biometrics represents a valid alternative but they suffer of drawbacks as well. Iris scanning, for example, is very reliable but too intrusive; fingerprints are socially accepted, but not applicable to non-consentient people. On the other hand, face recognition represents a good compromise between what’s socially acceptable and what’s reliable, even when operating under controlled conditions. In last decade, many algorithms based on linear/nonlinear methods, neural networks, wavelets, etc. have been proposed. Nevertheless, Face Recognition Vendor Test 2002 shown that most of these approaches encountered problems in outdoor conditions. This lowered their reliability compared to state of the art biometrics.
What is Face Recognition?
Face recognition technology is the least intrusive and fastest biometric technology. It works with the most obvious individual identifier –the human face.
Instead of requiring people to place their hand on a reader or precisely position their eye in front of a scanner, face recognition systems unobtrusively take pictures of people's faces as they enter a defined area. There is no intrusion or delay, and in most cases the subjects are entirely unaware of the process. They do not feel "under surveillance" or that their privacy has been invaded.
HISTORY:
Humans have always had the innate ability to recognize and distinguish between faces, yet computers only recently have shown the same ability. In the mid 1960s, scientists began work on using the computer to recognize human faces. Since then, facial recognition software has come a long way.
Identix®, a company based in Minnesota, is one of many developers of facial recognition technology. Its software, FaceIt®, can pick someone's face out of a crowd, extract the face from the rest of the scene and compare it to a database of stored images. In order for this software to work, it has to know how to differentiate between a basic face and the rest of the background. Facial recognition software is based on the ability to recognize a face and then measure the various features of the face.
Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up facial features. FaceIt defines these landmarks as nodal points. Each human face has approximately 80 nodal points. Some of these measured by the software are:
• Distance between the eyes
• Width of the nose
• Depth of the eye sockets
• The shape of the cheekbones
• The length of the jaw line
These nodal points are measured creating a numerical code, called a face print, representing the face in the database.
In the past, facial recognition software has relied on a 2D image to compare or identify another 2D image from the database. To be effective and accurate, the image captured needed to be of a face that was looking almost directly at the camera, with little variance of light or facial expression from the image in the database. This created quite a problem.
In most instances the images were not taken in a controlled environment. Even the smallest changes in light or orientation could reduce the effectiveness of the system, so they couldn't be matched to any face in the database, leading to a high rate of failure. In the next section, we will look at ways to correct the problem.
TECHNOLOGY
Our technology is based on neural computing and combines the advantages of elastic and neural networks.
Neural computing provides technical information processing methods that are similar to the way information is processed in biological systems, such as the human brain. They share some key strengths, like robustness fault-resistance and the ability to learn from examples. Elastic networks can compare facial landmarks even if images are not identical, as is practically always the case in real-world situations. Neural networks can learn to recognize similarities through pattern recognition.
3D FACIAL RECOGNITION
A newly-emerging trend in facial recognition software uses a 3D model, which claims to provide more accuracy. Capturing a real-time 3D image of a person's facial surface, 3D facial recognition uses distinctive features of the face -- where rigid tissue and bone is most apparent, such as the curves of the eye socket, nose and chin -- to identify the subject. These areas are all unique and don't change over time.
Using depth and an axis of measurement that is not affected by lighting, 3D facial recognition can even be used in darkness and has the ability to recognize a subject at different view angles with the potential to recognize up to 90 degrees (a face in profile).
Using the 3D software, the system goes through a series of steps to verify the identity of an individual.
Detection
Acquiring an image can be accomplished by digitally scanning an existing photograph (2D) or by using a video image to acquire a live picture of a subject (3D).
Alignment
Once it detects a face, the system determines the head's position, size and pose. As stated earlier, the subject has the potential to be recognized up to 90 degrees, while with 2D, the head must be turned at least 35 degrees toward the camera.
Measurement
The system then measures the curves of the face on a sub-millimeter (or microwave) scale and creates a template
01-04-2011, 09:53 AM
Post: #11
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT

.ppt  Face Recognition.ppt (Size: 408.5 KB / Downloads: 368)
Progress
 Understanding of related material is completed.
 Beginning of implementation has begun
DF-LDA Algorithm
The output are the Optimal Discriminant Features (ODF) which will be projected onto the DF-LDA based subspace.
The basic idea of complex mathematical computations in order To reduce the dimensions is to obtain better classification. The F-LDA step is incorporated in the case of closed classes to obtain the required ODF’s.
Feret Dataset
 Feret Dataset consists of facial images at different angles which are not normalized. Also includes data files which have feature data points (ground truths).
 Feature data points – includes x, y-axis locations of facial features such as left and right eyes, nose, and center of mouth.
Implementation preprocessing image files
 Each image in Feret dataset are bzipped and and in tiff image format.
 Using a simple batch script to unzip the files and convert tiff image files to PGM format using convert function from ImageMagick 5.5.7.
Implementation creating eye-coordinates file
 Each image in Feret has an associated .gnd file which lists feature data points.
 Perl script is used to extract left and right eye coordinates from each .gnd file and placed into an one eye coordinate file which lists image name and eye-coordinates.
Implementation normalization
 Normalization is done by reading each line in the eye coordinates file.
 These are the steps for normalization [4]:
 The image is scaled so as to make the distance between the eye's constant. In this step, the image is also cropped to a smaller size that will include essentially just the face. The standard FERET normalization crops the image to 150x130 pixels with 70 pixels between the centers of the eyes.
 A mask is applied that zeroes out pixels not in an oval that contains the typical face. Thus, hair, shirt collars, etc. are typically removed. The mask is generated analytically by specifying the dimensions of the masking oval.
 Histogram equalization is used to smooth the distribution of grey values for the non-masked pixels.
 The image is normalized so the non-masked pixels have mean zero and standard deviation one.
Implementation Image Lists
 Image Lists is a file which lists on each line the same-class normalized images.
 Using Perl, we parse the directory list of normalized images and create this file.
 This image list is a reference for training to know which images belong to a single class
Implementation What's next?
 Need to implement the DF-LDA algorithm for training using normalized files.
 Experimentation and Analysis of results.
01-04-2011, 12:10 PM
Post: #12
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
SUBMITTED BY-
SONIA SINGLA


.pptx  my ppt.pptx (Size: 1.2 MB / Downloads: 192)
FACE RECOGNITION TECHNOLOGY
INTRODUCTION TO FACE RECOGNITION TECHNOLOGY

Face recognition is a form of computer vision that uses faces to attempt to identify a person or verify person’s claimed identity.
FACE RECOGNITION SYSTEM COMES UNDER BIOMETRICS
BIOMETRICS

Any automatically measurable , robust, and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of the individual are called ‘biometrics’.
 based on human characteristics-
PHYSIOLOGICAL : face, iris, fingerprint
BEHAVIORAL : voice , signature

CLASSIFICATION
A biometrics system can be either an identification system or verification (authentication) sysem;
 IDENTIFICATION: ONE TO MANY - (1 to N)
find out who is ‘X’
 VERIFICATION : ONE TO ONE - (1 to 1)
answer the question is this ‘x’
ADVANTAGE OF BIOMETRICS
Usually verification in computer systems is done
based on measures like keys , cards , PIN and
Passwords . But these can be forgotten , destroyed disclosed or changed. A reliable and an accurate
Identification/verification is designed using ‘BIO-
METRICS ‘ technologies.
Biometrics are preferred over traditional passwords and PIN based technologies because-
 The person to be identified is required to be physically present at the time of identification.
 Identification based on biometrics technologies obviates the need to remember a password.
FACE RECOGNITION SYSTEMS
Face recognition systems are computer programs that are used for automatically
identifying a person.
Every face has numerous distinguishable landmarks,the different peaks and valleys that make up facial features.
Face recognition technology
measures -
 Distance between the eyes.
 width of nose.
 depth of eye socket.
 the shape of the cheekbones.
 the length of the jaw line.
input face image

face feature matching decision maker
database
output
Facial Recognition: Eigen face
Decompose face images into a small set of characteristic feature images.
A new face is compared to these stored images.
A match is found if the new faces is close to one of these images.
FACE RECOGNITION IS A FIVE STEP PROCESS
STEP 1 : ACQUIRING THE IMAGE
OF AN INDIVIDUAL’S
FACE.
there are two ways to acquire an image
a) Digitally scan an existing photograph.
b) acquire a live picture.
STEP 2 : LOCATE IMAGE OF FACE.
software is used to locate the faces in the image that has been obtained.
STEP 3 : ANALYSIS OF FACIAL IMAGE
 software measures face according to its peaks and valleys (nodal points).
 focuses on the inner region of the face .
STEP 4 : COMPARISON
the face print created by the software is compared to all face prints the system has stored in its database.
STEP 5 : MATCH OR NOT MATCH
software decides whether or not any comparison from step 4 are close enough to declare a possible match or not ?
WORKING
FEATURES OF FACE RECOGNITION SYSTEM

ENROLLMENT OF ALL INDIVIDUALS
IDENTIFICATION AND VARIFICATION OF INDIVIDUAL WHENEVER HE \SHE COMES TO THE CAMERA EVERYTIME
TWO DIMENSIONAL FACE RECOGNITION SYSTEMS
Before the advent of faster computers and sophisticated imaging software 2 d face recognition systems were used . The problem that arose from this type of systems was the fact that the person to be identified must be facing the camera at no more than 35 degrees for accurate identification.
THREE DIMENTIONAL FACE RECOGNITION SYSTEMS
The new facial recognition systems make use of 3-d images and are thus more accurate than theirpredecessors. these systems has ability to recognize face even when it is turned 90 degrees away
From the camera.
COMPARISON BETWEEN 2 D AND 3D FACE RECOGNITION SYSTEMS 2 D 3 D
2 d face recognition involves 3 d face recognition involves
intensity variations. shape variation.
2 d operate better under 3 d can operate in any
modest lighting conditions. circumstances.
2 d is affected by pose changes it is not affected by
as changes in facial gestures pose changes.
and expressions.
the person should be very close the person can be far
to camera. from the camera.
2 d is slower . 3 d is fast.
APPLICATIONS OF FACE RECOGNITION TECHNOLOGY
 AIRPORTS
 RAILWAY STATIONS
 MILLITARY
 FINANCIAL INSTITUTIONS (BANKS)
 GOVERNMENT OFFICES
 BUSINESS OF ALL KINDS
 CORPORATIONS
 ATM (AUTOMATIC TELLER MACHINE)
 INTERNET TRANSACTIONS
OTHER APPLICATIONS OF FACE RECOGNITION TECHNOLOGY
TIME ATTENDENCE SYSTEM :
face recognition system helps to keep each employee’s data accurately. In this technology we have to just enroll employee face and one unique id will be generated by the system. Every time when employee comes to office and goes out of office its time is recorded by the system by matching face.
VISITOR MANAGEMENT SYSTEM : for any organization effective visitor management system helps to keep our area safe from intrusion. Face recognition based visitor management systems provide full proof security by identifying each
visitor by face.
VOTER VARIFICATION : Mexican government made use of facial recognition systems to match their voters from their database to verify identity and minimize voter fraud.
ACCESS CONTROL SYSTEM : in today’s world everyone wants to secure their private area from external intrusion. Face recognition based access control systems gives full proof security solution by using human face.
06-04-2011, 12:27 PM
Post: #13
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
Presented By :
Navin Gupta


.ppt  Face Recognisation technology.ppt (Size: 999 KB / Downloads: 228)
Face Recognisation technology
Introduction

 The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face.
 This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication.
 Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences.
 Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes.
 Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins.
Biometrics
 A biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual’s identity.
 Biometrics can measure both physiological and behavioral characteristics.
 Physiological biometrics -based on measurements and data derived from direct measurement of a part of the human body.
 Behavioral biometrics -based on measurements and data derived from an action.
TYPES OF BIOMETRICS
 PHYSIOLOGICAL
a.Finger-scan
b. Facial Recognition
c. Iris-scan
d. Retina-scan
e. Hand-scan
 BEAVIORAL
a. Voice-scan
b. Signature-scan
c. Keystroke-scan
WHY WE CHOOSE FACE RECOGNITION TECHNOLOGY
 It requires no physical interaction on behalf of the user.
 It is accurate and allows for high enrolment and verification rates.
 It does not require an expert to interpret the comparison result.
 It can use your existing hardware infrastructure, existing camaras and image capture Devices will work with no problems
 It is the only biometric that allow you to perform passive identification in a one to.
FACE RECOGNITION
 face recognition there are two types of comparisons .
 VERIFICATION-in this the system compares the given individual with who that individual says they are and gives a yes or no decision.
 IDENTIFICATION- in this the system compares the given individual to all the Other individuals in the database and gives a ranked list of matches.
 All identification or authentication technologies operate using the following four stages:
 Capture: A physical or behavioural sample is captured by the system during Enrollment and also in identification or verification process.
 Extraction: unique data is extracted from the sample and a template is created.
 Comparison: the template is then compared with a new sample.
 Match/non match: the system decides if the features extracted from the new Samples are a match or a non match.
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
 The image is optical in characteristics and may be thought of as a collection of a large number of bright and dark areas representing the picture details.
 At an instant there will be large number of picture details existing simultaneously each representing the level of brightness of the scene to be reproduced.
 Therefore it would require infinite number of channels to transmit optical information corresponding to picture elements simultaneously.
 There is practical difficulty in transmitting all information simultaneously so we use a method called scanning.
 the conversion of optical information to electrical form and its transmission is carried out element by element one at a time in a sequential manner to cover the entire image.
WORKING OF VEDIO CAMERA
 A TV camera converts optical information into electrical information, the amplitude of which varies in accordance with variation of brightness.
 An optical image of the scene to be transmitted is focused by lense assembly on the rectangular glass plate of the camera tube.
 The inner side of this has a transparent coating on which is laid a very thin layer of photoconductive material. The photolayer has very high resistance when no light is falling on it but decreases depending on the intensity of light falling on it.
 An electron beam is formed by an electron gun in the TV camera tube.
 This beam is used to pick up the picture information now avilable on the target plate of varying resistace at each point.
 The electron beam is deflected by a pair of deflecting coils mounted on the glass envelope and kept mutually perpendicular to each other to achive scanning of the entire target area.
 The deflecting coils are fed seperately from two sweep oscillators, each operating at different frequencies.
 The magnetic deflection caused by current in one coil gives horizontal motion to the beam from left to right at a uniform rate and brings it back to the left side to commence the trace of the next line.
 The other coil is used to deflect the beam from top to bottom.
 As the beam moves from element to element it encounters different resistance across the target plate depending on the resistance of the photoconductive coating.
 The result is flow of current which varies in magnitude as elements are scanned.
 The current passes through the load resistance Rl connected to conductive coating on one side of the DC supply source on the other.
 Depending on the magnitude of current a varying voltage appears across the resistance Rl and this corresponds to the optical information of the picture
PERFORMANCE
 False acceptance rate (FAR) -The probability that a system will incorrectly identify an individual or will fail to reject an imposter. It is also called as type 2 error rate
FAR= NFA/NIIA
Where
NFA= number of false acceptance
NIIA= number of imposter identification attempts
 False rejection rates (FRR) -The probability that a system will fail to identify an enrollee. It is also called type 1 error rate.
 FRR= NFR/NEIA
where
 NFR= number of false rejection rates
 NEIA= number of enrollee identification attempt
 Response time: The time period required by a biometric system to return a decision on identification of a sample.
 decision Threshold: The acceptance or rejection of a data is dependent on the match score falling above or below the threshold. The threshold is adjustable so that the system can be made more or less strict; depending on the requirements of any given application.
 Enrollment time: The time period a person must spend to have his/her facial reference templatesuccessfully created.
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
The implementation of face recognition technology includes the following four stages:
 Data acquisition
 Input processing
 Face image classification
 decision making
Data acquisition
 The input can be recorded video of the speaker or a still image. A sample of 1 sec duration consists of a 25 frame video sequence.
 More than one camera can be used to produce a 3D representation of the face and to protect against the usage of photographs to gain unauthorized access.
Input processing
 A pre-processing module locates the eye position and takes care of the surrounding lighting condition and colour variance.
 First the presence of faces or face in a scene must be detected. Once the face is detected, it must be localized and Normalization process may be required to bring the dimensions of the live facial sample in alignment with the one on the template.
Face image classification
 Some facial recognition approaches use the whole face while others concentrate on facial components and/ or regions (such as lips, eyes etc).
 The appearance of the face can change considerably during speech and due to facial expressions. In particular the mouth is subjected to fundamental changes but is also very important source for discriminating faces.
 So an approach to person’s recognition is developed based on patio- temporal modeling of features extracted from talking face. Models are trained specific to a person’s speech articulate and the way that the person speaks.
 Person identification is performed by tracking mouth movements of the talking face and by estimating the likelyhood of each model of having generated the observed sequence of features.
 The model with the highest likelyhood is chosen as the recognized person. Synergetic computer are used to classify optical and audio features, respectively.
 A synergetic computer is a set of algorithm that simulate synergetic phenomena. In training phase the BIOID creates a prototype called faceprint for each person.
 A newly recorded pattern is preprocessed and compared with each faceprint stored in the database. As comparisons are made, the system assigns a value to the comparison using a scale of one to ten. If a score is above a predetermined threshold, a match is declared. .
 Face recognition starts with a picture, attempting to find a person in the image. This can be accomplished using several methods including movement, skin tones, or blurred human shapes. The face recognition system locates the head and finally the eyes of the individual.
 A matrix is then developed based on the characteristics of the Individual’s face. The method of defining the matrix varies according to the algorithm
 This matrix is then compared to matrices that are in a database and a similarity score is generated for each comparison.
 Artificial intelligence is used to simulate human interpretation of faces. In order to increase the accuracy and adaptability, some kind of machine learning has to be implemented.
METHOD OF CAPTURING
VIDEO IMAGING

Video imaging is more common as standard video cameras can be used. The precise position and the angle of the head and the surrounding lighting conditions may affect the system performance. The complete facial image is usually captured and a number of points on the face can then be mapped, position of the eyes, mouth and the nostrils as a example.
More advanced technologies make 3-D map of the face which multiplies the possible measurements that can be made.
THERMAL IMAGING
Thermal imaging has better accuracy as it uses facial temperature variations caused by vein structure as the distinguishing traits. As the heat pattern is emitted from the face itself without source of external radiation these systems can capture images despite the lighting condition, even in the dark.
The drawback is high cost. They are more expensive than standard video cameras.
HOW FACE RECOGNITION SYSTEMS WORK
 Facial recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If you look at the mirror, you can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features. Visionics defines these landmarks as nodal points. There are about 80 nodal points on a human face. Here are few nodal points that are measured by the software.
1. distance between the eyes
2. width of the nose
3. depth of the eye socket
4. cheekbones
5. jaw line
6. chin
THE SOFTWARE
 Detection-when the system is attached to a video surveilance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. The system switches to a high-resolution search only after a head-like shape is detected.
 Alignment-Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it.
 Normalization-The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.
 Representation-The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
 Matching- The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation.
 The heart of the FaceIt facial recognition system is the Local Feature Analysis (LFA) algorithm.
 This is the mathematical technique the system uses to encode faces.
 The system maps the face and creates a faceprint, a unique numerical code for that face. Once the system has stored a faceprint, it can compare it to the thousands or millions of faceprints stored in a database.
 Each faceprint is stored as an 84-byte file.
ADVANTAGES
 There are many benefits to face recognition systems such as its convinence and Social acceptability.all you need is your picturetaken for it to work.
 Face recognition is easy to use and in many cases it can be performed without a Person even knowing.
 Face recognition is also one of the most inexpensive biometric in the market and Its price should continue to go down.
DISADVANTAGES
 Face recognition systems can’t tell the difference between identical twins
APPLICATIONS
 Security/Counterterrorism. Access control, comparing surveillance images to Know terrorist.
 Day Care: Verify identity of individuals picking up the children.
 Residential Security: Alert homeowners of approaching personnel
 Voter verification: Where eligible politicians are required to verify their identity during a voting process this is intended to stop voting where the vote may not go as expected.
 Banking using ATM: The software is able to quickly verify a customer’s face.
18-04-2011, 04:48 PM
Post: #14
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
PRESENTED BY-
CHINTAN HARANIA
HARSHAL RAJGOR
ISHANI PARIKH
NEHA KHIMANI


.ppt  Face_Recognition tecnology.ppt (Size: 4.43 MB / Downloads: 175)
What is Biometrics ?
A biometric is a unique, measurable characteristics of a human being that can be used to automatically recognize an individual or verify an individuals identity.
Biometrics can measure Physiological and Behavioral characteristics
a. Physiological characteristics- Finger-scan, Iris scan , Hand scan, Retina scan.
b. Behavioral characteristics- Voice , Signature, and Keystroke scan.
Why Face Recognition Technology ?
 Requires no physical interaction on behalf of the user.
 It is accurate and allows for high enrollment and verification rates.
 It does not require any expert to interpret the comparison result.
 It uses existing hardware infrastructure devices.
 The only biometric which allow to perform passive identification in one to many environments.
 Face Detection algorithm using Colour and Geometric Information
 Feature extraction
 Template matching
Difficulties in face recognition
• Text is restricted to a limited number of characters.
• The content of images is inherently different and more complicated to deal with.
• Facial expressions.
• Cosmetics
• Ageing
• Lighting
Solution
• Acquire images of faces
• Store pixel data in arrays to obtain a face database
• Obtain test image
• Store pixel data as array
• Subtract test array from database arrays
• If result is a zero matrix then match is found
EXISTING FACE RECOGNITION METHODS
 Linear Discriminant Analysis
 Elastic Branch Group Matching
 Neural Networks
 PCA (Eigenfaces)
LDA
 Linear Discriminant Analysis predicts a categorical variable based on one or more metric independent variables.
 Linear Discriminant Analysis (LDA)
 Example
 Graph Interpretation
 Graphical Representation ctd.
 A 100% Accurate Discriminate Analysis
 Test results of LDA
 Test results of a subspace LDA-based face recognition method in 1999.
 E.B.G.M.
 Neural network technology
Face Recognition By Principal Component Analysis (PCA)
• PCA involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components.
• A statistical method for reducing the dimensionality of a data.
 Block Diagram of PCA Based Face Recognition System
 Original Image & Results Of Several Steps In Normalization Module
 Recognition Process
Steps For Recognition Procedure
• Collect a set of face images (Training Set).
• Calculate the Covariance matrix, and find its eigen values and eigenvectors.
• Select the desire eigen faces.
• For each new face, project it onto the face space, and find the distance to all the known face classes.
• If the person is recognized, the new face may enter the database as this person.
• If the face was not recognized, it may enter the database as new face class.
Advantages Of Face Recognition:
• Convenience & social acceptability.
• Can be performed secretly.
• Most inexpensive biometric.
• Easy to use.
Disadvantages Of Face Recognition:
• Can’t tell the difference between Identical twins.
• Performance degradation is due to poor lighting, sunglasses, long hair or other objects partially covering the subject’s face.
• Low resolution images because of data compression.
• Less effective if facial expressions vary.
• Problem of data collection update because of change of age.
APPLICATIONS OF FACE RECOGNITION
 BIOMETRICS – driver’s licenses, entitlement programs, immigration, national ID, passports, voter registration
 INFORMATION SECURITY – application security, desktop logon (windows NT, windows 95), database security, file encryption, intranet security, internet access, medical records, official company records, national records
 LAW ENFORCEMENT AND SURVEILLANCE – advanced video surveillance, CCTV control portal, post-event analysis
 SMART CARDS – stored value security, user authentication
 ACCESS CONTROL – facility access, vehicular access
CONCLUSION
Face recognition technologies have been associated generally with very costly top secure applications. Today the core technologies have evolved and the cost of equipments is going down dramatically due to the intergration and the increasing processing power. Certain applications of face recognition technology are now cost effective, reliable and highly accurate. As a result there are no technological or financial barriers for stepping from the pilot project to widespread deployment.
22-04-2011, 12:54 PM
Post: #15
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT

.doc  Face Recog.doc (Size: 60 KB / Downloads: 143)
Abstract:
Recently face recognition is attracting much attention in the society of network multimedia information access. Areas such as network security, content indexing and retrieval, and video compression benefits from face recognition technology because "people" are the center of attention in a lot of video. Network access control via face recognition not only makes hackers virtually impossible to steal one's "password", but also increases the user-friendliness in human-computer interaction. Indexing and/or retrieving video data based on the appearances of particular persons will be useful for users such as news reporters, political scientists, and moviegoers. For the applications of videophone and teleconferencing, the assistance of face recognition also provides a more efficient coding scheme. In this paper, we give an introductory course of this new information processing technology. The paper shows the readers the generic framework for the face recognition system, and the variants that are frequently encountered by the face recognizer. Several famous face recognition algorithms, such as eigenfaces and neural networks, will also be explained.
Introduction:
In today's networked world, the need to maintain the security of information or physical property is becoming both increasingly important and increasingly difficult. From time to time we hear about the crimes of credit card fraud, computer breaking’s by hackers, or security breaches in a company or government building. In the year 1998, sophisticated cyber crooks
caused well over US $100 million in losses (Reuters, 1999).In most of these crimes, the criminals were taking advantage of a fundamental flaw in the conventional access control systems: the systems do not grant access by "who we are", but by "what we have", such as ID cards, keys, passwords, PIN numbers, or mother's maiden name. None of these means are really define us. Rather, they merely are means to authenticate us. It goes without saying that if someone steals, duplicates, or acquires these identity means, he or she will be able to access our data or our personal property any time they want. Recently, technology became available to allow verification of "true" individual identity. This technology is based in a field called "biometrics". Biometric access control are automated methods of verifying or recognizing the identity of a living person on the basis of some physiological characteristics, such as fingerprints or facial features, or some aspects of the person's behavior, like his/her handwriting style or keystroke patterns. Since biometric systems identify a person by biological characteristics, they are difficult to forge. Among the various biometric ID methods, the physiological methods (fingerprint, face, DNA) are more stable than methods in behavioral category (keystroke, voice print). The reason is that physiological features are often non-alterable except by severe injury. The behavioral patterns, on the other hand, may fluctuate due to stress, fatigue, or illness. How-ever, behavioral IDs have the advantage of being non-intrusiveness. People are more comfortable signing their names or speaking to a microphone than placing their eyes before a scanner or giving a drop of blood for DNA sequencing. Face recognition is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness. It has the accuracy of a physiological approach without being intrusive.
Aim of the System:
The main objective of the report is to bridge the divide between a purely technical and a purely socio-political analysis of FRT. On the one side, there is a huge technical literature on algorithm development, grand challenges, vendor tests, etc., that talks in detail about the technical capabilities and features of FRT but does not really connect well with the challenges of real world installations, actual user requirements, or the background considerations that are relevant to situations in which these systems are embedded (social expectations, conventions, goals, etc.). On the other side, there is what one might describe as the “soft” social science literature of policy makers, media scholars, ethicists, privacy advocates, etc., which talks quite generally about biometrics and FRT, outlining the potential socio-political dangers of the technology. This literature often fails to get into relevant technical details and often takes for granted that the goals of biometrics and FRT are both achievable and largely Orwellian. Bridging these two literatures—indeed, points of view—is very important as FRT increasingly moves from the research laboratory into the world of socio-political concerns and practices. We intend this report to be a general and accessible account of FRT for informed readers. It is not a “state of the art” report on FRT. Although we have sought to provide sufficient detail in the account of the underlying technologies to serve as a foundation for our functional, moral, and political assessments, the technical description is not intended to be comprehensive.Nor is it a comprehensive socio-political analysis.Indeed, for a proper, informed debate on the socio-political implications of FRT, more detailed and publicly accessible in-situ studies are needed. The report should provide a sound basis from which to develop such in-situ studies. The report instead attempts to straddle the technical and the socio-political points of view without oversimplifying either.
Application scenarios for facial recognition systems (FRS):
The London Borough of Newham, in the UK, previously trialled a facial recognition system built into their borough-wide CCTV system.The German Federal Police use a facial recognition system to allow voluntary subscribers to pass fully automated border controls at Frankfurt Rhein-Main international airport. Subscribers need to be European Union or Swiss citizens.[citation needed] Since 2005 the German Federal Criminal Police Office offers centralized facial recognition on mugshot images for all German police agencies. Recognition systems are also used by casinos to catch card counters and other blacklisted individuals.The Australian Customs Service has an automated border processing system called SmartGate that uses facial recognition. The system compares the face of the individual with the image in the e-passport microchip, certifying that the holder of the passport is the rightful owner.Pennsylvania Justice Network searches crime scene photographs and CCTV footage in the mugshot database of previous arrests. A number of cold cases have been resolved since the system became operational in 2005. Other law enforcement agencies in the USA and abroad use arrest mugshot databases in their forensic investigative work.U.S. Department of State operates one of the largest face recognition systems in the world with over 75 million photographs that is actively used for visa processing.
In addition to being used for security systems, authorities have found a number of other applications for facial recognition systems. While earlier post 9/11 deployments were well publicized trials, more recent deployments are rarely written about due to their covert nature.At Super Bowl XXXV in January 2001, police in Tampa Bay, Florida, used Identix' facial recognition software, FaceIt, to search for potential criminals and terrorists in attendance at the event(it found 19 people with pending arrest warrants).In the 2000 presidential election, the Mexican government employed facial recognition software to prevent voter fraud. Some individuals had been registering to vote under several different names, in an attempt to place multiple votes. By comparing new facial images to those already in the voter database, authorities were able to reduce duplicate registrations.Similar technologies are being used in the United States to prevent people from obtaining fake identification cards and driver’s licenses.There are also a number of potential uses for facial recognition that are currently being developed. For example, the technology could be used as a security measure at ATM’s; instead of using a bank card or personal identification number, the ATM would capture an image of your face, and compare it to your photo in the bank database to confirm your identity. This same concept could also be applied to computers; by using a webcam to capture a digital image of yourself, your face could replace your password as a means to log-in.As part of the investigation of the disappearance of Madeleine McCann the British police are calling on visitors to the Ocean Club Resort, Praia da Luz in Portugal or the surrounding areas in the two weeks leading up to the child's disappearance on Thursday 3 May 2007 to provide copies of any photographs of people taken during their stay, in an attempt to identify the abductor using a biometric facial recognition application.Also, in addition to biometric usages, modern digital cameras often incorporate a facial detection system that allows the camera to focus and measure exposure on the face of the subject, thus guaranteeing a focused portrait of the person being photographed. Some cameras, in addition, incorporate a smile shutter, or take automatically a second picture if someone closed their eyes during exposure
11-05-2011, 04:17 PM
Post: #16
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
Submitted by
Himanshu Vats


.pptx  himanshu ppt.pptx (Size: 680.49 KB / Downloads: 186)

.docx  himanshu.docx (Size: 18.53 KB / Downloads: 202)
What is face recognition system?
Face Recognition system is an face matching system ..which is generally used in to encrypt the password .
Why Face Recognition?
Security
Fight terrorism
Personal information access
ATM
Home access (no keys or passwords)
man-machine interaction
Beauty search
What Is Difficult About Face Recognition??
Lighting variation
Orientation variation (face angle)
Size variation
Large database
General Image Types
Still image (digital photograph)
Digital camera
Dynamic image
Video camera
General Face Recognition Steps
Face Detection
Face Normalization
Face Identification
Face Detection
In General
Locate face in a given image
Separate it from the scene
Face Normalization
Adjustment
Expression
Rotation
Lighting
Scale
Eye location
Face Identification
Application of a face recognition algorithm
PCA Algorithms`
Principle Component Analysis
Look at the principal components of the distribution of faces, or the eigenvectors of the covariance matrix of the set of face images
Eigenfaces Algorithm
Eigenfaces Initialization
Acquire an initial set of face images (the training set)
Parameter Based Facial Recognition
Facial image is analyzed and reduced to small set of parameters describing prominent facial features
Major features analyzed are: eyes, nose, mouth and cheekbone curvature
These features are then matched to a database
Advantage: recognition task is not very expensive
Disadvantage: the image processing required is very expensive.
Template Based Facial Recognition
Salient regions of the facial image are extracted
These regions are then compared on a pixel-by-pixel basis with an image in the database
Advantage is that the image preprocessing is simpler
Disadvantage is the database search and comparison is very expensive
How the System Worked
Not Everyone Loves Face Recognition
To easy to misuse for wrong purposes
Technology is not accurate enough given the current technology and algorithms
Difficult to use.
Future Of Face Recognition
Advancements in hardware and software
Very large potential market
Remove its problem makes its more efficient..


Attached File(s)
.docx  report.docx (Size: 3.71 MB / Downloads: 144)
18-08-2011, 09:35 AM
Post: #17
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
Presented By:
Suvigya Tripathi
Ankit V. Gupta


.pptx  Face Recognition tech1.pptx (Size: 1.66 MB / Downloads: 121)
FACE RECOGNITOIN TECHNIQUE
INTRODUCTION
Steps:

Face Detection: differentiate a human face from the background of the image or a real time video.
Feature Detection: record its features.
Face Recognition: Compare it to a data base.
BLOCK DIAGRAM
WHAT IS FACE DETECTION

Technique employed to distinguish a Human face from the rest of the background of the image.
THE HISTORY
During 1964 and 1965, Bledsoe, along with Helen Chan and Charles Bisson , worked on using the computer to recognize human faces.
He was proud of this work, but because the funding was provided by an unnamed intelligence agency that did not allow much publicity, little of the work was published.
IMPORTANCE OF FACE DETECTION
The first step for any automatic face recognition system system.
First step in many Human Computer Interaction systems.
Expression Recognition
Cognitive State/Emotional State Recognition
First step in many surveillance and security systems.
Video coding
Automatic Target Recognition(ATR)
CHALLENGES
In – Plane Rotation
Out – Plane Rotation
Lighting
Aging Effects
Facial Expressions
Face Covered by
long Hairs or Hand.
CHALLENGES
2D – IMAGE SCAN

Different Approaches:
Knowledge Based Approach
Feature Invariant Method
Template Matching Method
KNOWLEDGE-BASED APPROACH
It uses human-coded rules to model facial features, such as two symmetric eyes, a nose in the middle and a mouth underneath the nose.
KNOWLEDGE-BASED APPROACH-SUMMARY
Pros:
Easy to come up with simple rules
Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified
Work well for face localization in uncluttered background
Cons:
Difficult to translate human knowledge into rules precisely: detailed rules fail to detect faces and general rules may find many false positives
Difficult to extend this approach to detect faces in different poses: implausible to enumerate all the possible cases
FEATURE INVARIANT METHOD
Feature invariant methods try to find facial features which are invariant to pose, lighting condition or rotation.
Skin colors, edges and shapes fall into this category.
FEATURE INVARIANT METHOD-NODAL POINT ANALYSIS
Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up the face
Distance between the eyes
Width of the nose
Depth of the eye sockets
The shape of the cheekbones
The length of the jaw line
FEATURE INVARIANT METHOD-SUMMARY
Pros:
Features are invariant to pose and change in orientation.
Cons:
Difficult to locate facial features due to several corruption (illumination, noise, occlusion)
Difficult to detect features in complex background
TEMPLATE MATCHING METHOD
Template matching methods calculate the correlation between a test image and a pre-selected facial templates.
TEMPLATE MATCHING METHOD-SUMMARY
Pros:
Simple
Cons:
Templates needs to be initialized near the face images
Difficult to enumerate templates for different poses (similar to knowledge-based methods)
BIOMETRICS SKIN TEXTURE ANALYSIS
Using skin color to find face segments is a vulnerable technique.
Non-animate objects with
the same color as skin can
be picked up since the
technique uses color
segmentation.
Then the face can be picked up using any of the approaches.
SKIN TEXTURE ANALYSIS: ADVANTAGES
Lack of restriction to orientation or size of faces.
A good algorithm can handle complex backgrounds.
It is relatively insensitive to changes in expression, including blinking, frowning or smiling
Has the ability to compensate for mustache or beard growth and the appearance of eyeglasses.
APPLICATIONS
Security measure at ATM’s
Digital Cameras
Public Surveillance (CCTV’s) at
Airports, Hospitals, etc.
Televisions and computers can
save energy by reducing the
brightness.
FACE RECOGNITION:OVERVIEW
A set of two task:
Face Identification: Given a face image that belongs to a person in a database, tell whose image it is.
Face Verification: Given a face image that might not belong to the database, verify whether it is from the person it is claimed to be in the database.
19-10-2011, 07:07 PM
Post: #18
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
this thread on face recognition techinques seminar report by HIMANSHU is awsome and is in format what i xactly required ...but after downloading its showing as doc corrupted can you please mail me the same 3.71mb file to
srinu.vankadari[at]gmail.com
20-10-2011, 09:24 AM
Post: #19
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
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20-10-2011, 03:55 PM
Post: #20
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
i want to download this
21-10-2011, 10:19 AM
Post: #21
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
to get information about the topic"FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT" refer the link bellow
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01-02-2012, 10:35 AM
Post: #22
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
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04-02-2012, 03:12 PM
Post: #23
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
i think finger print recognisation is better than this because no two persons have same finger prints Exclamation
30-04-2012, 02:48 PM
Post: #24
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
Facial Recognition Technology



.pdf  face.pdf (Size: 2.9 MB / Downloads: 93)

Purpose and scope of this report1.

This report is primarily addressed to three audiences: decision-makers in law enforcement and security considering the purchase, investment in, or implementation of facial recognition technology (FRT); policy makers considering how to regulate the development and uses of facial recognition and other biometric systems; and researchers who perform social or political analysis of technology.


Biometrics and identification in a global, 2. mobile world (“why is it important?”)

Although there has always been a need to identify individuals, the requirements of identification have changed in radical ways as populations have expanded and grown increasingly mobile. This is particularly true for the relationships between institutions and individuals, which are crucial to the well-being of societies, and necessarily and increasingly conducted impersonally—that is, without persistent direct and personal interaction. Importantly, these impersonal interactions include relationships between government and citizens for purposes of fair allocation of entitlements, mediated transactions with e-government, and security and law enforcement. Increasingly, these developments also encompass relationships between actors and clients or consumers based on financial transactions, commercial transactions, provision of services, and sales conducted among strangers, often mediated through the telephone, Internet, and the World Wide Web.


The development of FRS


In order to appreciate the complexity (and susceptibilities) of FRT, we need to get a sense of all the complex tasks that make up a system and how small variations in the system or environment can impact on these tasks. We will endeavor to keep the discussion on a conceptual level. However, from time to time, we will need to dig into some of the technical detail to highlight a relevant point. We will structure our discussion by starting with the key components (algorithms) of the system and then look at data and environment. The intention is to give the reader a general sense of the technology and some of the issues that emerge as a result of the technical design features and challenges, rather than providing a state of the art discussion.
07-05-2012, 04:59 PM
Post: #25
RE: FACE RECOGNITION TECHNOLOGY A SEMINAR REPORT
FACE RECOGNITION TECHNOLOGY



.pdf  orts_rpt.pdf (Size: 355 KB / Downloads: 236)

INTRODUCTION

This project deals with the topic of face recognition techniques using
digital image processing. Face recognition has always been a very
challenging task for the researches. On the one hand, its applications may
be very useful for personal verification and recognition. On the other hand, it
has always been very difficult to implement due to all different situation that
a human face can be found.[6] Nevertheless, the approaches of the last
decades have been determining for face recognition development. Due to
the difficulty of the face recognition task, the number of techniques is large
and diverse. In addition, the applications involve a huge number of
situations.
Although we can find many other identification and verification
techniques, the main motivation for face recognition is because it is
considered a passive, no intrusive system to verify and identify people.
There are many other types of identification such as password, PIN
(personal identification number) or token systems. Moreover, it is nowadays
very instilled the usage of fingerprints and iris as a physiological
identification system. They are very useful when we need an active
identification system; the fact that a person has to expose their body to some
device makes people feel being scanned and identified. The pause-anddeclare
interaction is the best method for bank transactions and security
areas; people feel conscious of it, as well as comfortable and safe with it.
However, we do not want to interact with people that way in many other
applications that required identification. For example, a store that wishes to
recognize some customers or a house that has to identify people that live in
there. For those application, face as well as voice verification are very
desirable. It is also important that an identification technique is closer to the
way human beings recognize each other. [5]
As it has already said previously, the applications for face recognition are
very varied. We can divide them into two big groups, the applications that
required face identification and the ones that need face verification. The
difference is that the first one uses a face to match with other one on a
database; on the other hand, the verification technique tries to verify a
human face from a given sample of that face.[6] Face recognition could be
also divided into two different groups, according to their field of application.
The main reason for promoting this technique is law enforcement
application; however, it can also be used for commercial application.
Among the law enforcement applications, some representative examples are

mug shot albums, video surveillance and shoplifting.[3] Concerning
commercial applications we can differentiate between entertainment (video
games, virtual reality and training programs), smart cards (driver’s license,
passport and voter registration) and information security (TV parental
control, cell phone and database security).[7]
It has already been stated that face recognition techniques have always
been a very challenging task for researches because of all difficulties and
limitations. Human faces are not an invariant characteristic; in fact, a
person’s face can change very much during short periods of time (from one
day to another) and because of long periods of time (a difference of months
or years). One problem of face recognition is the fact that different faces
could seem very similar; therefore, a discrimination task is needed. On the
other hand, when we analyze the same face, many characteristics may have
changed. Ones of the most important problems are changes in illumination,
variability in facial expressions, the presence of accessories (glasses,
beards, etc); finally, the rotation of a face may change many facial
characteristics. [6]

APPROACH

The paper principally deals with the comparison of two different methods
for face recognition. The project is based on two articles that describe these
two different techniques; they are attached at the references as source [3]
and [4]. These methods are “Face Recognition Using Eigenfaces” and “Face
recognition using line edge map”.
For each of the techniques, a short description of how it accomplishes the
described task will be given. Furthermore, some tables and results will be
showed in order to understand the accuracy of each method. This report
only shows a comparison of already made research studies; therefore, the
pictures used and the data are extracted from the original sources. Since
the methods do not follow a common line, they will be described separately;
it is due to the different ways to achieve face recognition. We could divide
face recognition techniques into two big groups: the ones that tackle the
problem using geometric approach, and the ones that use feature-based
characteristics. [6]

Finally, a discussion about the best characteristics of each method will
be carried out. Depending of the technique, and more important of the work
performed to make the article, different situation of face position, lighting, etc
will be commented. The main goal of this paper is find a good face
recognition technique depending on the situation.

WORK PERFORMED

During this section, the two face recognition algorithms will be explained
in order to understand the basis of each one. They have been selected
because two main reasons: they are very known and spread techniques for
face recognition; moreover, they represent different ways to approach the
problem as it was stated before. The first technique is based on the socalled
Karhunen-Loève transformation using eigenfaces for recognition. The
second one tries a new algorithm using line edge maps to improve the
previous methods such as the eigenfaces.

Face recognition using eigenfaces

As a general view, this algorithm extracts the relevant information of an
image and encodes it as efficiently as possible. For this purpose, a
collection of images from the same person is evaluated in order to obtain the
variation. Mathematically, the algorithm calculates the eigenvectors of the
covariance matrix of the set of face images. [4]
Each image from the set contribute to an eigenvector, these vectors
characterize the variations between the images. When we represent these
eigenvectors, we call it eigenfaces. Every face can be represented as a
linear combination of the eigenfaces; however, we can reduce the number of
eigenfaces to the ones with greater values, so we can make it more efficient.
The basic idea of the algorithm is develop a system that can compare not
images themselves, but these feature weights explained before. The
algorithm can be reduced to the next simple steps.
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