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

TL;DR

AuthNet introduces a novel authentication system combining facial recognition with unique temporal facial movements during password utterance, achieving high accuracy and robustness even with limited training data.

Contribution

This paper presents a new biometric authentication method leveraging temporal facial movements, enhancing security beyond traditional facial recognition.

Findings

Achieved 98.1% accuracy on MIRACL-VC1 dataset.

Effective with only 10 training samples per user.

Outperformed existing facial recognition and lip reading models.

Abstract

Biometric systems based on Machine learning and Deep learning are being extensively used as authentication mechanisms in resource-constrained environments like smartphones and other small computing devices. These AI-powered facial recognition mechanisms have gained enormous popularity in recent years due to their transparent, contact-less and non-invasive nature. While they are effective to a large extent, there are ways to gain unauthorized access using photographs, masks, glasses, etc. In this paper, we propose an alternative authentication mechanism that uses both facial recognition and the unique movements of that particular face while uttering a password, that is, the temporal facial feature movements. The proposed model is not inhibited by language barriers because a user can set a password in any language. When evaluated on the standard MIRACL-VC1 dataset, the proposed model achieved an accuracy of 98.1%, underscoring its effectiveness as an effective and robust system. The proposed method is also data-efficient since the model gave good results even when trained with only 10 positive video samples. The competence of the training of the network is also demonstrated by benchmarking the proposed system against various compounded Facial recognition and Lip reading models.