| Previous | [ 1] | [ 2] | [ 3] | [ 4] | [ 5] | [ 6] | [ 7] | [ 8] | [ 9] | [ 10] | [ 11] | [ 12] | [ 13] | [ 14] | [ 15] | [ 16] | [ 17] | [ 18] |
¡@
Jing-Wein Wang and Wen-Yuan Chen+
Institute of Photonics and Communications
National Kaohsiung University of Applied Sciences
Kaohsiung, 807 Taiwan
+Department of Electronic Engineering
National Chin-Yi University of Technology
Taichung, 411 Taiwan
Fingerprint images that are captured by optical readers usually cannot locate a fingerprint
since the rotation and the translation are always together. Considering that both
real-time and accurate requirements are need for live applications, this paper presents a
novel approach to recognizing a fingerprint based on the core sub-region, which is the
area of 100 ¡Ñ 100 pixels surrounding the core point. Log-polar mapping is used to extract
the translation-invariant features derived from the discrete wavelet frame transform.
Finally, a Bayesian likelihood ratio-based fitness function is devised to genetically select
the most discriminative log-polar feature subset by disregarding redundant features via
support vector machines classification. The classification results are given for real fingerprint
data. Experimental results show that the proposed method can reject imposters
efficiently and achieve an over 98% recognition rate operating with two frames/s processing
speed. In comparison to the related works, the proposed system is more accurate
than the conventional minutiae-based methods.
Received April 3, 2007; revised October 4, 2007 & March 6, 2008; accepted April 3, 2008.
Communicated by Tong-Yee Lee.
* This paper was partially supported by the National Science Council of Taiwan, R.O.C. under grant No. NSC
96-2221-E-151-051.