University of Oulu

U. Muhammad and M. Oussalah, "Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets," 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), Waikoloa Beach, HI, USA, 2023, pp. 1-6, doi: 10.1109/FG57933.2023.10042547.

Self-supervised face presentation attack detection with dynamic grayscale snippets

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Author: Usman, Muhammad1; Oussalah, Mourad1
Organizations: 1Center for Machine Vision and Signal Analysis (CMVS), University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-08-16


Face presentation attack detection (PAD) plays an important role in defending face recognition systems against presentation attacks. The success of PAD largely relies on supervised learning that requires a huge number of labeled data, which is especially challenging for videos and often requires expert knowledge. To avoid the costly collection of labeled data, this paper presents a novel method for self-supervised video representation learning via motion prediction. To achieve this, we exploit the temporal consistency based on three RGB frames which are acquired at three different times in the video sequence. The obtained frames are then transformed into grayscale images where each image is specified to three different channels such as R(red), G(green), and B(blue) to form a dynamic grayscale snippet (DGS). Motivated by this, the labels are automatically generated to increase the temporal diversity based on DGS by using the different temporal lengths of the videos, which prove to be very helpful for the downstream task. Benefiting from the self-supervised nature of our method, we report the results that outperform existing methods on four public benchmarks, namely, Replay-Attack, MSU-MFSD, CASIA-FASD, and OULU-NPU. Explainability analysis has been carried out through LIME and Grad-CAM techniques to visualize the most important features used in the DGS.

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ISBN: 979-8-3503-4544-5
ISBN Print: 979-8-3503-4545-2
Pages: 1 - 6
DOI: 10.1109/FG57933.2023.10042547
Host publication: 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG)
Conference: International Conference on Automatic Face and Gesture Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
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