ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces

Anudeep Vurity, Emanuela Marasco, Raghavendra Ramachandra, Jongwoo Park; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5316-5325

Finger photo Presentation Attack Detection (PAD) can significantly strengthen smartphone device security. Still these algorithms are trained to detect certain attack types. Furthermore they are designed to operate on images acquired by specific capture devices which leads to poor generalization and a lack of robustness in handling mobile hardware's evolving nature. The proposed investigation is the first to systematically analyze the performance degradation of existing deep learning PAD systems convolutional and transformers in cross-capture device settings. In this paper we introduce the ColFigPhotoAttnNet architecture designed based on window attention on color channels followed by the nested residual network as the predictor to achieve a reliable PAD. Extensive experiments using various capture devices including iPhone13 Pro GooglePixel 3 Nokia C5 and OnePlusOne were carried out to evaluate the performance of proposed and existing methods on three publicly available databases. The findings underscore the effectiveness of our approach.