13-09-2013, 04:14 PM
Fingerprint Verification using the Texture of Fingerprint Image
Verification using the Texture .pdf (Size: 336.49 KB / Downloads: 19)
Abstract
In this paper, a fingerprint verification method is
presented that improves matching accuracy by overcoming
the shortcomings of previous methods due to missing some
minutiae, non-linear distortions, and rotation and distortion
variations. It reduces multi-spectral noise by enhancing a
fingerprint image to accurately and reliably determine a
reference point and then extract a 129 X 129 block, making
the reference point its center. From the 4 co-occurrence
matrices four statistical descriptors are computed.
Experimental results show that the proposed method is more
accurate than other methods the average false acceptance
rate (FAR) is 0.62%, the average false rejection rate (FRR)
is 0.08%, and the equal error rate (EER) is 0.35%.
Introduction
Biometrics fingerprints are probably the most widely
used personal identification tool, as they have been used
for many centuries due to their individuality, uniqueness
and reliability. A distinctive feature of fingerprints lies in
the high degree of difficulty in terms of forgery, along
with the fact that fingerprints are unique to each person;
this means that fingerprints provide an excellent source of
entropy, which makes fingerprinting an excellent
candidate for security applications. Users cannot pass
their fingerprint characteristics to other users as easily as
they do with their cards or passwords [1;2]. The pattern of
the valleys and ridges on human fingertips forms the
fingerprint image. Analyzing this pattern at different level
reveals different types of features. Methods to extract and
match fingerprint features can be classified into three
categories: minutiae-based, correlation-based, and hybrid
[3].
Proposed Method
Fingerprint Image Enhancement
Fingerprint images are not always good quality; in real
life, skin condition, sensor noise, and incorrect finger
pressure produce low-quality images. In order to have a
good quality image, enhancement is used to improve the
contrast between ridges and valleys in the fingerprint
images Figure 1 shows original images; Figure 2 shows
enhanced images. The enhancement method [12] consists
of the following steps:
Extracting the reference point block & orientation
normalization
After locating the reference point, a 129 X 129 bock is
extracted from the image, making the reference point the
center of the bock. The image is then aligned by rotating it
with an angle of 0° to ensure that the reference point of
each rotated image has a zero orientation. Figure 4 shows
the rotated 129 X 129 block and the reference point at its
center. This unifying process is performed with rotation in
order to avoid the time-consuming rotation and translation
alignments of previous algorithms [12; 13] and to achieve
higher matching accuracy.
Conclusion
This paper proposes a novel method to verify
enhanced fingerprint images using co-occurrence matrices
and four descriptors. The experimental results
demonstrate that the proposed algorithm exhibits excellent
performance for verifying enhanced fingerprint images.