Face Anti-spoofing and Liveliness detection using Mobile net and Haar cascade Algorithm

Abstract

A common biometric approach is another way of face recognition techniques. The method is convenient, easy access to the user and direct comparison with other methods of face recognition due to its rapid development in recent years. Resisting spoofing attacks made on face recognition systems requires Face Anti-Spoofing Systems (FAS) techniques. FAS based on deep learning perform exceptionally well and dominates this area with the emergence of large-scale academic datasets in recent years. The data requirements for training effective anti-spoofing models in this field, however, are large, and there is no way to perform live spoofing. Our paper proposes a combined method of face liveliness detection using Haar-Cascade algorithms and mobile-net classifiers. As a contribution to stimulating future research, we present an overview of technique like deep learning-based FASs. It covers numerous novel and insightful Anti-spoofing approach structured using the modules,1) Eye opening action evaluates with the blinking eye systems and 2) mobile-net classifier module which makes use of a pre-trained version. We wrap up our study with gradually merged these two modules and added them to a basic facial recognition system. Software Python-based results comparisons between classifiers are used to explain efficiency of the suggested approach.