Asymmetric Modality Translation For Face Presentation Attack Detection

TL;DR

This paper introduces a novel asymmetric modality translation framework for face presentation attack detection that improves generalization across different attack types and illumination conditions, achieving state-of-the-art results.

Contribution

It proposes a new asymmetric modality translation approach with a fusion scheme and illumination normalization, enhancing cross-domain face PAD performance.

Findings

Effective in detecting various attack types

Achieves state-of-the-art performance on public datasets

Improves robustness under different illumination conditions

Abstract

Face presentation attack detection (PAD) is an essential measure to protect face recognition systems from being spoofed by malicious users and has attracted great attention from both academia and industry. Although most of the existing methods can achieve desired performance to some extent, the generalization issue of face presentation attack detection under cross-domain settings (e.g., the setting of unseen attacks and varying illumination) remains to be solved. In this paper, we propose a novel framework based on asymmetric modality translation for face presentation attack detection in bi-modality scenarios. Under the framework, we establish connections between two modality images of genuine faces. Specifically, a novel modality fusion scheme is presented that the image of one modality is translated to the other one through an asymmetric modality translator, then fused with its corresponding paired image. The fusion result is fed as the input to a discriminator for inference. The training of the translator is supervised by an asymmetric modality translation loss. Besides, an illumination normalization module based on Pattern of Local Gravitational Force (PLGF) representation is used to reduce the impact of illumination variation. We conduct extensive experiments on three public datasets, which validate that our method is effective in detecting various types of attacks and achieves state-of-the-art performance under different evaluation protocols.