Back to Search Start Over

Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features.

Authors :
Jain, Anil K.
Yi Chen
Demirkus, Meltem
Source :
IEEE Transactions on Pattern Analysis & Machine Intelligence; Jan2007, Vol. 29 Issue 1, p15-27, 13p, 18 Black and White Photographs, 4 Graphs
Publication Year :
2007

Abstract

Fingerprint friction ridge details are generally described in a hierarchical order at three different levels, namely, Level 1 (pattern), Level 2 (minutia points), and Level 3 (pores and ridge contours). Although latent print examiners frequently take advantage of Level 3 features to assist in identification, Automated Fingerprint Identification Systems (AFIS) currently rely only on Level 1 and Level 2 features. In fact, the Federal Bureau of Investigation's (FBI) standard of fingerprint resolution for AFIS is 500 pixels per inch (ppi), which is inadequate for capturing Level 3 features, such as pores. With the advances in fingerprint sensing technology, many sensors are now equipped with dual resolution (500 ppi/1,000 ppi) scanning capability. However, increasing the scan resolution alone does not necessarily provide any performance improvement in fingerprint matching, unless an extended feature set is utilized. As a result, a systematic study to determine how much performance gain one can achieve by introducing Level 3 features in AFIS is highly desired. We propose a hierarchical matching system that utilizes features at all the three levels extracted from 1,000 ppi fingerprint scans. Level 3 features, including pores and ridge contours, are automatically extracted using Gabor filters and wavelet transform and are locally matched using the Iterative Closest Point (ICP) algorithm. Our experiments show that Level 3 features carry significant discriminatory information. There is a relative reduction of 20 percent in the equal error rate (EER) of the matching system when Level 3 features are employed in combination with Level 1 and 2 features. This significant performance gain is consistently observed across various quality fingerprint images. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01628828
Volume :
29
Issue :
1
Database :
Complementary Index
Journal :
IEEE Transactions on Pattern Analysis & Machine Intelligence
Publication Type :
Academic Journal
Accession number :
23472730
Full Text :
https://doi.org/10.1109/TPAMI.2007.250596