TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection

TL;DR

This paper introduces TransRPPG, a transformer-based framework that automatically learns liveness features from remote photoplethysmography maps for 3D mask face presentation attack detection, enhancing accuracy and efficiency.

Contribution

It proposes a novel deep learning method that automatically extracts intrinsic liveness features from rPPG data, reducing reliance on manual feature design.

Findings

Effective in intra- and cross-dataset tests

Lightweight model suitable for mobile devices

Achieves high detection accuracy

Abstract

3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks. Recently, remote photoplethysmography (rPPG) has been developed as an intrinsic liveness clue for 3D mask PAD without relying on the mask appearance. However, the rPPG features for 3D mask PAD are still needed expert knowledge to design manually, which limits its further progress in the deep learning and big data era. In this letter, we propose a pure rPPG transformer (TransRPPG) framework for learning intrinsic liveness representation efficiently. At first, rPPG-based multi-scale spatial-temporal maps (MSTmap) are constructed from facial skin and background regions. Then the transformer fully mines the global relationship within MSTmaps for liveness representation, and gives a binary prediction for 3D mask detection. Comprehensive experiments are conducted on two benchmark datasets to demonstrate the efficacy of the TransRPPG on both intra- and cross-dataset testings. Our TransRPPG is lightweight and efficient (with only 547K parameters and 763M FLOPs), which is promising for mobile-level applications.