Robust face-voice based speaker identity verification using multilevel fusion
Image and Vision Computing
Reliability score based multimodal fusion for biometric person authentication
MATH'08 Proceedings of the American Conference on Applied Mathematics
Gabor Filter-Based Fingerprint Anti-spoofing
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Ridgelet-based fake fingerprint detection
Neurocomputing
Wavelet-based multiresolution analysis of ridges for fingerprint liveness detection
International Journal of Information and Computer Security
A Novel Region Based Liveness Detection Approach for Fingerprint Scanners
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Liveness detection of fingerprint based on band-selective Fourier spectrum
ICISC'07 Proceedings of the 10th international conference on Information security and cryptology
Liveness and spoofing in fingerprint identification: issues and challenges
CEA'10 Proceedings of the 4th WSEAS international conference on Computer engineering and applications
Vitality detection in fingerprint identification
WSEAS Transactions on Information Science and Applications
Classification of fingerprint images to real vs. spoof
International Journal of Biometrics
Experimental results on fingerprint liveness detection
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Proceedings of the on Multimedia and security
Vitality detection from fingerprint images: a critical survey
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Multi-scale local binary pattern with filters for spoof fingerprint detection
Information Sciences: an International Journal
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Fingerprint scanners can be spoofed by artificial fingers using moldable plastic, clay, Play-Doh, gelatin, silicone rubber materials, etc. 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. In this paper, a new method based on the wavelet transform on the ridge signal extracted along the ridge mask is proposed which can detect the perspiration phenomenon using only a single image. Statistical features are extracted for multiresolution scales to discriminate between live and non-live fingers. Based on these features, we use a classification tree to generate the decision rules for the liveness classification. We test this method on the dataset which contains about 58 live, 80 spoof (50 made from Play-Doh and 30 made from gelatin), and 25 cadaver subjects for 3 different scanners. Also, we test this method on a second dataset which contains 33 live and 33 spoof (made from gelatin) subjects. The proposed liveness detection method is purely software based and application of this method can provide anti-spoofing protection for fingerprint scanners.