Six biometric devices point the finger at security
Network Computing
Biometrical fingerprint recognition: don't get your fingers burned
Proceedings of the fourth working conference on smart card research and advanced applications on Smart card research and advanced applications
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Biometrics
Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
Time-series detection of perspiration as a liveness test in fingerprint devices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fighting coercion attacks in key generation using skin conductance
USENIX Security'10 Proceedings of the 19th USENIX conference on Security
Robustness evaluation of biometric systems under spoof attacks
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Combining perspiration- and morphology-based static features for fingerprint liveness detection
Pattern Recognition Letters
Large scale experiments on fingerprint liveness detection
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Multi-scale local binary pattern with filters for spoof fingerprint detection
Information Sciences: an International Journal
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It has been shown that fingerprint scanners can be deceived very easily, using simple, inexpensive techniques. In this work, a countermeasure against such attacks is enhanced, that utilizes a wavelet based approach to detect liveness, integrated with the fingerprint matcher. Liveness is determined from perspiration changes along the fingerprint ridges, observed only in live people. The proposed algorithm was applied to a data set of approximately 58 live, 50 spoof and 28 cadaver fingerprint images captured at 0 and 2s, from each of three different types of scanners, for normal conditions. The results demonstrate perfect separation of live and not live for the normal conditions. Without liveness module the commercially available verifinger matcher is shown to give equal error rate (EER) of 13.85% where false reject rate is calculated for genuine-live users and false accept rate is for genuine-not live, imposter-live and imposter-not live. The integrated system of fingerprint matcher and liveness module reduces EER to 0.03%. Results are also presented for moist and dry fingers simulated by glycerin and acetone, respectively. The system is further tested using gummy fingers and various deliberately simulated conditions including pressure change and adding moisture to the spoof to analyze the strength of the liveness algorithm.