Fingerprint Identification and Verification System using Minutiae Matching
| |
Fingerprint Identification and Verification System using Minutiae Matching
 Fingerprint Identification and Verification System using Minutiae Matching.pdf (Size: 375.78 KB / Downloads: 47)
Abstract:
Fingerprints are the most widely used biometric
feature for person identification and verification in the
field of biometric identification. Fingerprints possess two
main types of features that are used for automatic
fingerprint identification and verification: (i) global ridge
and furrow structure that forms a special pattern in the
central region of the fingerprint and (ii) Minutiae details
associated with the local ridge and furrow structure. This
paper presents the implementation of a minutiae based
approach to fingerprint identification and verification and
serves as a review of the different techniques used in
various steps in the development of minutiae based
Automatic Fingerprint Identification Systems (AFIS). The
technique conferred in this paper is based on the
extraction of minutiae from the thinned, binarized and
segmented version of a fingerprint image. The system uses
fingerprint classification for indexing during fingerprint
matching which greatly enhances the performance of the
matching algorithm. Good results (~92% accuracy) were
obtained using the FVC2000 fingerprint databases.
1. INTRODUCTION
Fingerprints have been in use for biometric recognition
since long because of their high acceptability,
immutability and individuality. Immutability refers to the
persistence of the fingerprints over time whereas
individuality is related to the uniqueness of ridge details
across individuals. The probability that two fingerprints
are alike is 1 in 1.9 x 1015 [1]. These features make the use
of fingerprints extremely effective in areas where the
provision of a high degree of security is an issue. The
major steps involved in automated fingerprint recognition
include a) Fingerprint Acquisition, b) Fingerprint
Segmentation, c) Fingerprint Image Enhancement, d)
Feature Extraction e) Minutiae Matching, f) Fingerprint
Classification.
Fingerprint acquisition can either be offline (inked) or
Online (Live scan). In the inked method an imprint of an
inked finger is first obtained on a paper, which is then
scanned. This method usually produces images of very
poor quality because of the non-uniform spread of ink and
is therefore not exercised in online AFIS. For online
fingerprint image acquisition, capacitative or optical
fingerprint scanners such as URU 4000, etc. are utilized
which make use of techniques such as frustrated total
internal reflection (FTIR) [2], ultrasound total internal
reflection [3], sensing of differential capacitance [4] and
non contact 3D scanning [5] for image development. Live
scan scanners offer much greater image quality, usually a
resolution of 512 dpi, which results in superior reliability
during matching in comparison to inked fingerprints.
Segmentation refers to the separation of fingerprint area
(foreground) from the image background [6].
Segmentation is useful to avoid extraction of features in
the noisy areas of fingerprints or the background. A
Simple thresholding technique [7] proves to be ineffective
because of the streaked nature of the fingerprint area. The
presence of noise in a fingerprint image requires more
vigorous techniques for effective fingerprint segmentation.
A good segmentation method should exhibit the following
characteristics [8]: |
|
|
