Presentation Attack Detection Methods based on Gaze Tracking and Pupil Dynamic: A Comprehensive Survey

TL;DR

This comprehensive survey reviews gaze tracking and pupil dynamic methods for presentation attack detection in biometric systems, highlighting current techniques, challenges, and future directions for enhancing security against spoofing.

Contribution

It provides an extensive analysis of existing gaze and pupil-based liveness detection methods, summarizing state-of-the-art algorithms, datasets, and standardization efforts.

Findings

Gaze tracking and pupil dynamics are promising for contactless biometric security.

Current algorithms effectively detect presentation attacks with high accuracy.

Open challenges include dataset accessibility and standardization in the field.

Abstract

Purpose of the research: In the biometric community, visible human characteristics are popular and viable for verification and identification on mobile devices. However, imposters are able to spoof such characteristics by creating fake and artificial biometrics to fool the system. Visible biometric systems have suffered a high-security risk of presentation attack. Methods: In the meantime, challenge-based methods, in particular, gaze tracking and pupil dynamic appear to be more secure methods than others for contactless biometric systems. We review the existing work that explores gaze tracking and pupil dynamic liveness detection. The principal results: This research analyzes various aspects of gaze tracking and pupil dynamic presentation attacks, such as state-of-the-art liveness detection algorithms, various kinds of artifacts, the accessibility of public databases, and a summary of standardization in this area. In addition, we discuss future work and the open challenges to creating a secure liveness detection based on challenge-based systems.