Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection

TL;DR

This paper introduces a large-scale high-fidelity mask dataset and a contrastive context-aware learning framework to improve face presentation attack detection in real-world scenarios.

Contribution

The paper presents a new high-fidelity mask dataset and a novel contrastive learning method that leverages rich contextual information for enhanced PAD performance.

Findings

Effective detection of high-fidelity mask attacks demonstrated.

Superior performance on multiple 3D mask datasets.

Robustness across different sensors and mask types.

Abstract

Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to real-world applications, we introduce a largescale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask). Specifically, a total amount of 54,600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors. Together with the dataset, we propose a novel Contrastive Context-aware Learning framework, namely CCL. CCL is a new training methodology for supervised PAD tasks, which is able to learn by leveraging rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks. Extensive experimental evaluations on HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of our method.