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Biometric Liveness Detection: Challenges and Research Opportunities


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Keywords:aml data,    kyc jumio,    explain kyc,    fintech aml compliance,    pep sanction screening,  onfido verification


Aliveness Detection for IRIS Biometrics
With momentum building for biometrics, it may not be long before consumers are using their fingerprints, irises, or voices to prove who they are to bank Web sites and automated teller machines. Visa International and the Financial Services Technology Consortium are expected to announce Friday that they will participate in a six-month test of fingerprint scanning, facial scanning, and electronic signature verification. The New York-based International Biometric Group will conduct the tests from July through yearend. Biometric identification has already gotten a boost this month from Microsoft Corp.'s announcement that it will incorporate biometrics into future versions of its Windows
Fingerprint has been extensively used for biometric recognition around the world. However, fingerprints are not secrets and an adversary can synthesis a fake finger to spoof the biometric system. The mainstream of the current fingerprint spoof detection methods are basically binary classifier trained on some real and fake samples. While they perform well on detecting fake samples created by using the same methods used for training, their performance degrades when encountering fake samples created by a novel spoofing method. In this paper, we approach the problem from a different perspective by incorporating ECG. Compare with the conventional biometrics, stealing someone's ECG is far more difficult if not impossible. Considering that ECG is a vital signal and motivated by its inherent liveness, we propose to combine it with a fingerprint liveness detection algorithm. The combination is natural as both ECG and fingerprint can be captured from fingertips. In the proposed framework, ECG and fingerprint are combined not only for authentication purpose but also for liveness detection. We also examine automatic template updating using ECG and fingerprint. In addition, we propose a stopping criterion that reduces the average waiting time for signal acquisition. We have performed extensive experiments on LivDet2015 database which is presently the latest available liveness detection database and compare the proposed method with six liveness detection methods as well as twelve participants of LivDet2015 competition. The proposed system has achieved a liveness detection EER of 4.2% incorporating only 5 seconds of ECG. By extending the recording time to 30 seconds, liveness detection EER reduces to 2.6% which is about 4 times better than the best of six comparison methods. This is also about 2 times better than the best results achieved by participants of LivDet2015 competition.

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