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.
CITATION STYLE
Raghavendra, M., Omprakash, P., & Mukesh, B. R. (2021). AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract). In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 18, pp. 15873–15874). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v35i18.17933
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