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1 January 2008 New approach for liveness detection in fingerprint scanners based on valley noise analysis
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Recent research has shown that it is possible to spoof a variety of fingerprint scanners using some simple techniques with molds made from plastic, clay, Play-Doh, silicon, or gelatin materials. To protect against spoofing, methods of liveness detection measure physiological signs of life from fingerprints, ensuring that only live fingers are captured for enrollment or authentication. We propose a new liveness detection method based on noise analysis along the valleys in the ridge-valley structure of fingerprint images. Unlike live fingers, which have a clear ridge-valley structure, artificial fingers have a distinct noise distribution due to the material’s properties when placed on a fingerprint scanner. Statistical features are extracted in multiresolution scales using the wavelet decomposition technique. Based on these features, liveness separation (live/nonlive) is performed using classification trees and neural networks. We test this method on the data set, that contains about 58 live, 80 spoof (50 made from Play-Doh and 30 made from gelatin), and 25 cadaver subjects for 3 different scanners. We also test this method on a second data set that contains 28 live and 28 spoof (made from silicon) subjects. Results show that we can get approximately 90.9–100% classification of spoof and live fingerprints. The proposed liveness detection method is purely software-based, and application of this method can provide antispoofing protection for fingerprint scanners.
©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
Bozhao Tan and Stephanie Caswell Schuckers "New approach for liveness detection in fingerprint scanners based on valley noise analysis," Journal of Electronic Imaging 17(1), 011009 (1 January 2008).
Published: 1 January 2008

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