[09-10]kyc section-Digital KYC Solutions

Culture & biometrics: regional differences in the perception of biometric authentication technologies


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An Object-Face Liveness Detection based on Local Feature Descriptors Using Fuz-SVM Classifier
The article reports on the growing market for biometric security, which now covers credit unions and community banks. Biometric technology has been used by financial institutions to control access in safety deposit boxes and to secure computer networks. Aite Group indicates that 35% of financial institutions will embrace biometric technologies by 2009. The technology substitutes passwords by fingerprint scanning and blocks unauthorized network access.
Fingerprint-based recognition systems have been increasingly deployed in various applications nowadays. However, the recognition systems can be spoofed by using an accurate imitation of a live fingerprint such as an artificially made fingerprint. In this paper, we propose a novel software-based fingerprint liveness detection method which achieves good detection accuracy. We regard the fingerprint liveness detection as a two-class classification problem and construct co-occurrence array from image gradients to extract features. In doing so, the quantization operation is firstly conducted on the images. Then, the horizontal and vertical gradients at each pixel are calculated, and the gradients of large absolute values are truncated into a reduced range. Finally, the second-order and the third-order co-occurrence arrays are constructed from the truncated gradients, and the elements of the co-occurrence arrays are directly used as features. The second-order and the third-order co-occurrence array features are separately utilized to train support vector machine classifiers on two publicly available databases used in Fingerprint Liveness Detection Competition 2009 and 2011. The experimental results have demonstrated that the features extracted with the third-order co-occurrence array achieve better detection accuracy than that with the second-order co-occurrence array and outperform the state-of-the-art methods.

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