Unified Face Matching and Physical-Digital Spoofing Attack Detection

Arun Kunwar, Ajita Rattani; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1429-1439

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.