Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing
Interference factors in visible light image data, such as backgrounds and lighting, often lead to poor performance of RGB-based single-modality face anti-spoofing methods. To address these limitations, we propose an innovative face anti-spoofing framework. Within this framework, we design a convolut...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10855450/ |
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| author | Nana Li Zhipeng Weng Fangmei Liu Zuhe Li Wei Wang |
| author_facet | Nana Li Zhipeng Weng Fangmei Liu Zuhe Li Wei Wang |
| author_sort | Nana Li |
| collection | DOAJ |
| description | Interference factors in visible light image data, such as backgrounds and lighting, often lead to poor performance of RGB-based single-modality face anti-spoofing methods. To address these limitations, we propose an innovative face anti-spoofing framework. Within this framework, we design a convolutional neural network (CNN) based on the Dual-path Adaptive Channel Attention (DACA) module, aiming to filter the features of the input facial images to extract key information. In addition, we develop feature constraints method based on Inner Similarity Estimation (ISE), which effectively enhances intra-class consistency by reducing the distance between samples and their class center. This method narrows the intra-class sample distribution and improves class separability, preventing the model from learning excessive irrelevant information and enhancing the robustness and generalization of face anti-spoofing. We test our method on the CASIA SURF dataset, CASIA SURF-CeFA dataset, and CASIA FASD dataset, which shows that our method has significant advantages in distinguishing between live and spoofed faces. |
| format | Article |
| id | doaj-art-94b200416ef1428b9bbd727c643ebf12 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-94b200416ef1428b9bbd727c643ebf122025-02-07T00:01:25ZengIEEEIEEE Access2169-35362025-01-0113228552286710.1109/ACCESS.2025.353490610855450Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-SpoofingNana Li0https://orcid.org/0009-0003-6232-2195Zhipeng Weng1Fangmei Liu2Zuhe Li3https://orcid.org/0000-0002-2511-3226Wei Wang4https://orcid.org/0000-0002-0707-8076College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaCollege of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaCollege of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaCollege of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaDepartment of Computing, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaInterference factors in visible light image data, such as backgrounds and lighting, often lead to poor performance of RGB-based single-modality face anti-spoofing methods. To address these limitations, we propose an innovative face anti-spoofing framework. Within this framework, we design a convolutional neural network (CNN) based on the Dual-path Adaptive Channel Attention (DACA) module, aiming to filter the features of the input facial images to extract key information. In addition, we develop feature constraints method based on Inner Similarity Estimation (ISE), which effectively enhances intra-class consistency by reducing the distance between samples and their class center. This method narrows the intra-class sample distribution and improves class separability, preventing the model from learning excessive irrelevant information and enhancing the robustness and generalization of face anti-spoofing. We test our method on the CASIA SURF dataset, CASIA SURF-CeFA dataset, and CASIA FASD dataset, which shows that our method has significant advantages in distinguishing between live and spoofed faces.https://ieeexplore.ieee.org/document/10855450/Face anti-spoofingattention mechanismfeature constraintconvolutional neural network |
| spellingShingle | Nana Li Zhipeng Weng Fangmei Liu Zuhe Li Wei Wang Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing IEEE Access Face anti-spoofing attention mechanism feature constraint convolutional neural network |
| title | Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing |
| title_full | Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing |
| title_fullStr | Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing |
| title_full_unstemmed | Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing |
| title_short | Dual-Path Adaptive Channel Attention Network Based on Feature Constraints for Face Anti-Spoofing |
| title_sort | dual path adaptive channel attention network based on feature constraints for face anti spoofing |
| topic | Face anti-spoofing attention mechanism feature constraint convolutional neural network |
| url | https://ieeexplore.ieee.org/document/10855450/ |
| work_keys_str_mv | AT nanali dualpathadaptivechannelattentionnetworkbasedonfeatureconstraintsforfaceantispoofing AT zhipengweng dualpathadaptivechannelattentionnetworkbasedonfeatureconstraintsforfaceantispoofing AT fangmeiliu dualpathadaptivechannelattentionnetworkbasedonfeatureconstraintsforfaceantispoofing AT zuheli dualpathadaptivechannelattentionnetworkbasedonfeatureconstraintsforfaceantispoofing AT weiwang dualpathadaptivechannelattentionnetworkbasedonfeatureconstraintsforfaceantispoofing |