3D Face Anti-spoofing with Factorized Bilinear Coding

TL;DR

This paper introduces a novel 3D face anti-spoofing method using factorized bilinear coding that effectively distinguishes real faces from realistic 3D attacks by analyzing subtle fine-grained differences across multiple color spaces.

Contribution

The paper proposes a new anti-spoofing approach based on factorized bilinear coding of multiple color channels, and introduces a large-scale wax figure face database for 3D face presentation attack detection.

Findings

Achieves state-of-the-art performance on multiple face spoofing datasets.

Effectively detects realistic 3D face presentation attacks.

Utilizes discriminative features from RGB and YCbCr spaces.

Abstract

We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. When compared with widely studied 2D face presentation attacks, 3D face spoofing attacks are more challenging because face recognition systems are more easily confused by the 3D characteristics of materials similar to real faces. In this work, we tackle the problem of detecting these realistic 3D face presentation attacks, and propose a novel anti-spoofing method from the perspective of fine-grained classification. Our method, based on factorized bilinear coding of multiple color channels (namely MC\_FBC), targets at learning subtle fine-grained differences between real and fake images. By extracting discriminative and fusing complementary information from RGB and YCbCr spaces, we have developed a principled solution to 3D face spoofing detection. A large-scale wax figure face database (WFFD) with both images and videos has also been collected as super-realistic attacks to facilitate the study of 3D face presentation attack detection. Extensive experimental results show that our proposed method achieves the state-of-the-art performance on both our own WFFD and other face spoofing databases under various intra-database and inter-database testing scenarios.