Robust Face Liveness Detection for Biometric Authentication using Single Image

TL;DR

This paper introduces a lightweight CNN-based method for face liveness detection that effectively identifies various presentation attacks, ensuring faster and more secure biometric authentication.

Contribution

It presents a novel, efficient CNN architecture for robust face liveness detection and introduces a new dataset with over 500 videos for attack evaluation.

Findings

High accuracy in detecting print/display, video, and wrap attacks

Fast processing time of 1-2 seconds on CPU

Validated effectiveness on a newly created spoof attack dataset

Abstract

Biometric technologies are widely adopted in security, legal, and financial systems. Face recognition can authenticate a person based on the unique facial features such as shape and texture. However, recent works have demonstrated the vulnerability of Face Recognition Systems (FRS) towards presentation attacks. Using spoofing (aka.,presentation attacks), a malicious actor can get illegitimate access to secure systems. This paper proposes a novel light-weight CNN framework to identify print/display, video and wrap attacks. The proposed robust architecture provides seamless liveness detection ensuring faster biometric authentication (1-2 seconds on CPU). Further, this also presents a newly created 2D spoof attack dataset consisting of more than 500 videos collected from 60 subjects. To validate the effectiveness of this architecture, we provide a demonstration video depicting print/display, video and wrap attack detection approaches. The demo can be viewed in the following link: https://rak.box.com/s/m1uf31fn5amtjp4mkgf1huh4ykfeibaa