University of Oulu

Z. Yu, X. Li, P. Wang and G. Zhao, "TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection," in IEEE Signal Processing Letters, vol. 28, pp. 1290-1294, 2021, doi: 10.1109/LSP.2021.3089908

TransRPPG : remote photoplethysmography transformer for 3D mask face presentation attack detection

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Author: Yu, Zitong1; Li, Xiaobai1; Wang, Pichao2;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90014, Finland
2Alibaba Group, Bellevue, WA, 98004, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-06


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 547 K parameters and 763 M FLOPs), which is promising for mobile-level applications.

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Series: IEEE signal processing letters
ISSN: 1070-9908
ISSN-E: 1558-2361
ISSN-L: 1070-9908
Volume: 28
Pages: 1290 - 1294
DOI: 10.1109/LSP.2021.3089908
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported by the Academy of Finland for project MiGA (grant 316765), ICT 2023 project (grant 328115), Infotech Oulu, project 6+E (grant 323287) funded by Academy of Finland, and project PhInGAIN (grant 200414) funded by The Finnish Work Environmental Fund.
Academy of Finland Grant Number: 316765
Detailed Information: 316765 (Academy of Finland Funding decision)
328115 (Academy of Finland Funding decision)
323287 (Academy of Finland Funding decision)
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