Dual-cross central difference network for face anti-spoofing |
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Author: | Yu, Zitong1; Qin, Yunxiao2; Zhao, Hengshuang3; |
Organizations: |
1CMVS, University of Oulu 2Northwestern Polytechnical University 3University of Oxford |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.4 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2021110453716 |
Language: | English |
Published: |
International Joint Conferences on Artificial Intelligence Organization,
2021
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Publish Date: | 2021-11-04 |
Description: |
AbstractFace anti-spoofing (FAS) plays a vital role in securing face recognition systems. Recently, central difference convolution (CDC) has shown its excellent representation capacity for the FAS task via leveraging local gradient features. However, aggregating central difference clues from all neighbors/directions simultaneously makes the CDC redundant and sub-optimized in the training phase. In this paper, we propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively. It is interesting to find that, with only five ninth parameters and less computational cost, C-CDC even outperforms the full directional CDC. Based on these two decoupled C-CDC, a powerful Dual-Cross Central Difference Network (DC-CDN) is established with Cross Feature Interaction Modules (CFIM) for mutual relation mining and local detailed representation enhancement. Furthermore, a novel Patch Exchange (PE) augmentation strategy for FAS is proposed via simply exchanging the face patches as well as their dense labels from random samples. Thus, the augmented samples contain richer live/spoof patterns and diverse domain distributions, which benefits the intrinsic and robust feature learning. Comprehensive experiments are performed on four benchmark datasets with three testing protocols to demonstrate our state-of-the-art performance. see all
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ISBN Print: | 978-0-9992411-9-6 |
Pages: | 1281 - 1287 |
DOI: | 10.24963/ijcai.2021/177 |
OADOI: | https://oadoi.org/10.24963/ijcai.2021/177 |
Host publication: |
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) |
Conference: |
International Joint Conferences on Artificial Intelligence Organization |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
113 Computer and information sciences |
Subjects: | |
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 328115 323287 200414 |
Detailed Information: |
316765 (Academy of Finland Funding decision) 328115 (Academy of Finland Funding decision) 323287 (Academy of Finland Funding decision) 200414 (Academy of Finland Funding decision) |
Copyright information: |
© 2021 International Joint Conferences on Artificial Intelligence. |