DynamicLip: Shape-Independent Continuous Authentication via Lip Articulator Dynamics

TL;DR

This paper introduces a novel lip-based continuous authentication system that leverages lip articulator dynamics to achieve shape-independent, robust, and privacy-preserving biometric verification with high accuracy and attack resistance.

Contribution

It proposes a new shape-independent lip authentication method based on articulator motion, addressing limitations of static lip shape reliance and speech variability.

Findings

Achieves 99.06% accuracy in diverse environments.

Demonstrates robustness against mimic and deepfake attacks.

Validates effectiveness with a dataset of 50 subjects.

Abstract

Biometrics authentication has become increasingly popular due to its security and convenience; however, traditional biometrics are becoming less desirable in scenarios such as new mobile devices, Virtual Reality, and Smart Vehicles. For example, while face authentication is widely used, it suffers from significant privacy concerns. The collection of complete facial data makes it less desirable for privacy-sensitive applications. Lip authentication, on the other hand, has emerged as a promising biometrics method. However, existing lip-based authentication methods heavily depend on static lip shape when the mouth is closed, which can be less robust due to lip shape dynamic motion and can barely work when the user is speaking. In this paper, we revisit the nature of lip biometrics and extract shape-independent features from the lips. We study the dynamic characteristics of lip biometrics based on articulator motion. Building on the knowledge, we propose a system for shape-independent continuous authentication via lip articulator dynamics. This system enables robust, shape-independent and continuous authentication, making it particularly suitable for scenarios with high security and privacy requirements. We conducted comprehensive experiments in different environments and attack scenarios and collected a dataset of 50 subjects. The results indicate that our system achieves an overall accuracy of 99.06% and demonstrates robustness under advanced mimic attacks and AI deepfake attacks, making it a viable solution for continuous biometric authentication in various applications.