Liveness detection for biometric authentication in mobile applications

The vulnerability of biometric authentication systems to spoofing attacks is now a widely accepted fact. A spoofing attack occurs when an impostor attempts to masquerade as genuine user by falsifying biometric data and thereby gaining illegitimate access. Several liveness detection methods have been proposed, which consist in determining whether there is a live person in front of the biometric sensor or an artificial replica. But, the problem is still unresolved owing to high level difficulty in determining efficient features with low computational cost to detect the spoofing attacks. In addition, existing methods are not particularly targeted for liveness detection in mobile biometric applications, thus mainly inapplicable for portable devices. Hence, we present a multi-biometric approach, that can detect face, iris and fingerprint spoofing attacks in mobile applications, by employing a novel real-time feature description based on order permutations, named Locally Uniform Comparison Image Descriptor (LUCID). LUCID is computable in linear time with respect to number of pixels and does not require floating point computation, beside the fact that typical mobile devices perform poorly for floating point applications. Our approach is therefore exclusively simple, fast and efficient, making it thus highly suitable for mobile devices. Moreover, contrary to existing schemes, our method utilize the same lone image descriptor technique effectively for three biometric traits, i.e. face, iris and fingerprint, liveness detection. Additionally, our method uses only one image for liveness detection, which can also be used for recognition. Experiments on publicly available face, iris and fingerprint data sets with real spoofing attacks show promising results.