@article{Raghavendra_Omprakash_B R_2021, title={AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract)}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17933}, DOI={10.1609/aaai.v35i18.17933}, abstractNote={Deep learning algorithms are widely used to extend modern biometric authentication mechanisms in resource-constrained environments like smartphones, providing ease-of-use and user comfort, while maintaining a non-invasive nature. In this paper, an alternative is proposed, that uses both facial recognition and the unique movements of that particular face while uttering a password. The proposed model is language independent, the password doesn’t necessarily need to be a set of meaningful words or numbers, and also, is a contact-less system. When evaluated on the standard MIRACL-VC1 dataset, the proposed model achieved a testing accuracy of 98.1%, underscoring its effectiveness.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Raghavendra, Mohit and Omprakash, Pravan and B R, Mukesh}, year={2021}, month={May}, pages={15873-15874} }