ML Anthology

AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract)

Mohit Raghavendra, Pravan Omprakash, B. R. Mukesh
AAAI 2021 pp. 15873-15874
doi:10.1609/AAAI.V35I18.17933 /aaai/2021/raghavendra2021aaai-authnet/

Abstract

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.

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Cite

Text

Raghavendra et al. "AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17933

Markdown

[Raghavendra et al. "AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/raghavendra2021aaai-authnet/) doi:10.1609/AAAI.V35I18.17933

BibTeX

@inproceedings{raghavendra2021aaai-authnet,
  title     = {{AuthNet: A Deep Learning Based Authentication Mechanism Using Temporal Facial Feature Movements (Student Abstract)}},
  author    = {Raghavendra, Mohit and Omprakash, Pravan and Mukesh, B. R.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15873-15874},
  doi       = {10.1609/AAAI.V35I18.17933},
  url       = {https://mlanthology.org/aaai/2021/raghavendra2021aaai-authnet/}
}