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Proceedings Paper

Comparison of ridge- and intensity-based perspiration liveness detection methods in fingerprint scanners
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Paper Abstract

Fingerprint scanners can be spoofed by fake fingers using moldable plastic, clay, Play-Doh, wax or gelatin. Liveness detection is an anti-spoofing method which can detect physiological signs of life from fingerprints to ensure only live fingers can be captured for enrollment or authentication. Our laboratory has demonstrated that the time-varying perspiration pattern can be used as a measure to detect liveness for fingerprint systems. Unlike spoof or cadaver fingers, live fingers have a distinctive spatial perspiration phenomenon both statically and dynamically. In this paper, a new intensity based approach is presented which quantifies the grey level differences using histogram distribution statistics and finds distinct differences between live and non-live fingerprint images. Based on these static and dynamic features, we generate the decision rules to perform liveness classification. These methods were tested on optical, capacitive DC and electro-optical scanners using a dataset of about 58 live fingerprints, 50 spoof (made from Play-Doh and Gelatin) and 25 cadaver fingerprints. The dataset was divided into three sets: training set, validation set and test set. The training set was used to generate the classification tree model while the best tree model was decided by the validation set. Finally, the test set was used to estimate the performance. The results are compared with the former ridge signal algorithm with new extracted features. The outcome shows that the intensity based approach and ridge signal approach can extract simple features which perform with excellent classification (about 90%~100%) for some scanners using a classification tree. The proposed liveness detection methods are purely software based, efficient and easy to be implemented for commercial use. Application of these methods can provide anti-spoofing protection for fingerprint scanners.

Paper Details

Date Published: 17 April 2006
PDF: 10 pages
Proc. SPIE 6202, Biometric Technology for Human Identification III, 62020A (17 April 2006); doi: 10.1117/12.666415
Show Author Affiliations
Bozhao Tan, Clarkson Univ. (United States)
Stephanie Schuckers, Clarkson Univ. (United States)


Published in SPIE Proceedings Vol. 6202:
Biometric Technology for Human Identification III
Patrick J. Flynn; Sharath Pankanti, Editor(s)

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