Face anti‐spoofing from the perspective of data sampling. Issue 1 (11th December 2022)

• Record Type: Journal Article
• Authors: Muhammad, Usman; Oussalah, Mourad
• Is Part Of: Electronics letters. Volume 59:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-11
• Subjects: image and vision processing and display technology -- image classification -- image motion analysis -- image processing -- image recognition; Electronics -- Periodicals; 621.381
• DOI: 10.1049/ell2.12692
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract: Without deploying face anti‐spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Thus, face presentation attack detection (PAD) plays a vital role in providing secure facial access to digital devices. Most existing video‐based PAD countermeasures lack the ability to cope with long‐range temporal variations in videos. Moreover, the key‐frame sampling prior to the feature extraction step has not been widely studied in the face anti‐spoofing domain. To mitigate these issues, this paper provides a data sampling approach by proposing a video processing scheme that models the long‐range temporal variations based on Gaussian weighting function (GWF). Specifically, the proposed scheme encodes the consecutive t frames of video sequences into a single RGB image based on a Gaussian‐weighted summation of the t frames. Using simply the data sampling scheme alone, it is demonstrated here that state‐of‐the‐art performance can be achieved without any bells and whistles in both intra‐database and inter‐database testing scenarios for the three public benchmark datasets; namely, replay‐Attack, MSU‐MFSD, and CASIA‐FASD. In particular, the proposed scheme provides a much lower error (from 15.2% to 7.6% on CASIA‐FASD and 5.9% to 4.9% on replay‐attack) compared to baselines in cross‐database scenarios. Abstract : We present a data‐driven approach to capture the appearance and dynamics of video into a single RGB image. Our analysis shows that the proposed temporal modeling can amplify important clues, e.g., hand movements, and surface edges, to improve the detection accuracy. Moreoever, it is extensible and can be plugged into different deep learning‐based face PAD models.