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Biometric systems based on Machine learning and Deep learning are beingextensively used as authentication mechanisms in resource-constrainedenvironments like smartphones and other small computing devices. TheseAI-powered facial recognition mechanisms have gained enormous popularity inrecent years due to their transparent, contact-less and non-invasive nature.While they are effective to a large extent, there are ways to gain unauthorizedaccess using photographs, masks, glasses, etc. In this paper, we propose analternative authentication mechanism that uses both facial recognition and theunique movements of that particular face while uttering a password, that is,the temporal facial feature movements. The proposed model is not inhibited bylanguage barriers because a user can set a password in any language. Whenevaluated on the standard MIRACL-VC1 dataset, the proposed model achieved anaccuracy of 98.1%, underscoring its effectiveness as an effective and robustsystem. The proposed method is also data-efficient since the model gave goodresults even when trained with only 10 positive video samples. The competenceof the training of the network is also demonstrated by benchmarking theproposed system against various compounded Facial recognition and Lip readingmodels.

AuthNet: A Deep Learning based Authentication Mechanism using Temporal Facial Feature Movements