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Directed Gaze Trajectories for Biometric Presentation Attack Detection.

Asad Ali¹, Sanaul Hoque¹, Farzin Deravi¹

¹School of Engineering and Digital Arts, University of Kent, Canterbury CT2 7NT, UK.

Sensors (Basel, Switzerland)

|March 6, 2021

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This study introduces a novel method using moving visual stimuli to detect biometric spoofing attacks by analyzing eye movements. The approach effectively distinguishes genuine user interactions from presentation attack artefacts.

Area of Science:

Biometrics and Security
Human-Computer Interaction
Computer Vision

Background:

Biometric systems are vulnerable to presentation attacks using artefacts like photos or masks.
Detecting these sophisticated spoofing attempts is crucial for maintaining system security.

Purpose of the Study:

To propose and evaluate a novel method for detecting biometric presentation attacks.
To leverage stimulated eye movements using randomized visual stimuli for enhanced spoofing detection.

Main Methods:

Utilized visual stimuli with randomized trajectories to induce and capture pupillary motion.
Tested various challenge trajectories on different device geometries.
Collected pupillary movement data from 80 volunteers during genuine and spoofing attempts using photo, 2D, and 3D mask artefacts.

Keywords:

biometrics face recognition gaze tracking presentation attack detection sensor-level spoofing

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Main Results:

The proposed method demonstrated potential in differentiating genuine user interactions from spoofing attempts.
Analysis of pupillary movement data showed significant differences between real and artificial presentations.
The system successfully identified various types of presentation attack artefacts.

Conclusions:

Stimulated eye movements via randomized visual challenges offer a promising feature for biometric presentation attack detection.
This technique can enhance the robustness of biometric systems against sophisticated spoofing methods.