Biometric authentication is now being used ubiquitously as an alternative to passwords on mobile devices. However, current biometric systems are vulnerable to simple spoofing attacks. Several liveness detection methods have been proposed to determine whether there is a live person or an artificial replica in front of the biometric sensor. Yet, the problem is unsolved due to hardship in finding discriminative and computationally inexpensive features for spoofing attacks. Moreover, previous liveness detection approaches are not explicitly aimed for mobile biometric, thus principally unsuited for portable devices. Therefore, we build a software-based multi-biometric prototype that detects face, iris and fingerprint spoofing attacks on mobile devices. We present MoBio_LivDet (Mobile Biometric Liveness Detection), a novel approach that analyzes local features and global structures of the biometric images using a set of low-level feature descriptors and decision level fusion. The system allows user to balance the security level (robustness against spoofing) and convenience that they want. The proposed method is highly fast, simple, efficient, robust and does not require user-cooperation, thus making it extremely apt for mobile devices. Experimental analysis on publicly available face, iris and fingerprint data sets with real spoofing attacks show promising results.
