Unified Face Matching and Physical-Digital Spoofing Attack Detection

TL;DR

This paper presents a unified model combining face recognition and spoof attack detection using a Swin Transformer backbone and HiLo attention, improving robustness and efficiency against physical and digital spoofing attacks.

Contribution

The paper introduces a novel unified model that integrates face recognition and spoof detection, leveraging advanced attention mechanisms and augmentation techniques for enhanced robustness.

Findings

Effective in unified face recognition and spoof detection

Resilient against unseen spoofing attacks

Outperforms separate models in efficiency and accuracy

Abstract

Face recognition technology has dramatically transformed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating HiLo attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model robustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications.