Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets

TL;DR

This paper introduces a self-supervised video representation learning method for face presentation attack detection using dynamic grayscale snippets, reducing reliance on labeled data and outperforming existing methods on multiple benchmarks.

Contribution

The novel approach leverages motion prediction with grayscale snippets and automatic label generation, advancing self-supervised PAD without extensive labeled datasets.

Findings

Outperforms existing methods on four public benchmarks.

Uses self-supervised learning to reduce labeled data dependency.

Provides explainability analysis with LIME and Grad-CAM.

Abstract

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.