{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T17:33:37Z","timestamp":1789666417093,"version":"4.0.1"},"reference-count":53,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Henan Provincial Science and Technology Research Project","award":["252102211052"],"award-info":[{"award-number":["252102211052"]}]},{"name":"Henan Provincial Science and Technology Research Project","award":["242102211020"],"award-info":[{"award-number":["242102211020"]}]},{"name":"Henan Provincial Science and Technology Research Project","award":["222102210010"],"award-info":[{"award-number":["222102210010"]}]},{"name":"Henan Provincial Science and Technology Research Project","award":["23XNKJTD0205"],"award-info":[{"award-number":["23XNKJTD0205"]}]},{"name":"Science and Technology Innovation Project of Zhengzhou University of Light Industry","award":["252102211052"],"award-info":[{"award-number":["252102211052"]}]},{"name":"Science and Technology Innovation Project of Zhengzhou University of Light Industry","award":["242102211020"],"award-info":[{"award-number":["242102211020"]}]},{"name":"Science and Technology Innovation Project of Zhengzhou University of Light Industry","award":["222102210010"],"award-info":[{"award-number":["222102210010"]}]},{"name":"Science and Technology Innovation Project of Zhengzhou University of Light Industry","award":["23XNKJTD0205"],"award-info":[{"award-number":["23XNKJTD0205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Face anti-spoofing detection is crucial for identity verification and security monitoring. However, existing single-modal models struggle with feature extraction under complex lighting conditions and background variations. Moreover, the feature distributions of live and spoofed samples often overlap, resulting in suboptimal classification performance. To address these issues, we propose a jointly optimized framework integrating the Enhanced Channel Attention (ECA) mechanism and the Intra-Class Differentiator (ICD). The ECA module extracts features through deep convolution, while the Bottleneck Reconstruction Module (BRM) employs a channel compression\u2013expansion mechanism to refine spatial feature selection. Furthermore, the channel attention mechanism enhances key channel representation. Meanwhile, the ICD mechanism enforces intra-class compactness and inter-class separability, optimizing feature distribution both within and across classes, thereby improving feature learning and generalization performance. Experimental results show that our framework achieves average classification error rates (ACERs) of 2.45%, 1.16%, 1.74%, and 2.17% on the CASIA-SURF, CASIA-SURF CeFA, CASIA-FASD, and OULU-NPU datasets, outperforming existing methods.<\/jats:p>","DOI":"10.3390\/jimaging11040116","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T08:46:20Z","timestamp":1744274780000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Face Anti-Spoofing Based on Adaptive Channel Enhancement and Intra-Class Constraint"],"prefix":"10.3390","volume":"11","author":[{"given":"Ye","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2609-2574","authenticated-orcid":false,"given":"Wenzhe","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuhe","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Keresh, A., and Shamoi, P. (2024). Liveness Detection in Computer Vision: Transformer-Based Self-Supervised Learning for Face Anti-Spoofing. arXiv.","DOI":"10.1109\/ACCESS.2024.3513795"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Almeida, W.R., Andal\u00f3, F.A., Padilha, R., Bertocco, G., Dias, W., Torres, R.D.S., Wainer, J., and Rocha, A. (2020). Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0238058"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Schwartz, W.R., Rocha, A., and Pedrini, H. (2011, January 11\u201313). Face spoofing detection through partial least squares and low-level descriptors. Proceedings of the 2011 International Joint Conference on Biometrics (IJCB), Washington, DC, USA.","DOI":"10.1109\/IJCB.2011.6117592"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yu, Z., Zhao, C., Zhu, X., Qin, Y., Zhou, Q., Zhou, F., and Lei, Z. (2020, January 13\u201319). Deep spatial gradient and temporal depth learning for face anti-spoofing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00509"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1109\/TIFS.2023.3296330","article-title":"Fm-vit: Flexible modal vision transformers for face anti-spoofing","volume":"18","author":"Liu","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liao, C.H., Chen, W.C., Liu, H.T., Yeh, Y.R., Hu, M.C., and Chen, C.S. (2023, January 3\u20137). Domain invariant vision transformer learning for face anti-spoofing. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV56688.2023.00604"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, A., and Liang, Y. (2023). Ma-vit: Modality-agnostic vision transformers for face anti-spoofing. arXiv.","DOI":"10.24963\/ijcai.2022\/165"},{"key":"ref_8","first-page":"5609","article-title":"Deep learning for face anti-spoofing: A survey","volume":"45","author":"Yu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pan, G., Sun, L., Wu, Z., and Lao, S. (2007, January 14\u201321). Eyeblink-based anti-spoofing in face recognition from a generic webcamera. Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4409068"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2268","DOI":"10.1109\/TIFS.2016.2578288","article-title":"Secure face unlock: Spoof detection on smartphones","volume":"11","author":"Patel","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Komulainen, J., Hadid, A., and Pietik\u00e4inen, M. (October, January 29). Context based face anti-spoofing. Proceedings of the 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), Washington, DC, USA.","DOI":"10.1109\/BTAS.2013.6712690"},{"key":"ref_12","unstructured":"Freitas, P.T., Anjos, A., De Martino, J.M., and Marcel, S. (2012, January 5\u20139). LBP\u2212TOP based countermeasure against face spoofing attacks. Proceedings of the Asian Conference on Computer Vision (ACCV 2012), Daejeon, Republic of Korea."},{"key":"ref_13","first-page":"141","article-title":"Face antispoofng using speeded-up robust features and fisher vector encoding","volume":"24","author":"Boulkenafet","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., and Li, S.Z. (April, January 29). A face antispoofing database with diverse attacks. Proceedings of the 2012 5th IAPR International Conference on Biometrics (ICB), New Delhi, India.","DOI":"10.1109\/ICB.2012.6199754"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TIFS.2015.2398817","article-title":"Deep representations for iris, face, and fingerprint spoofing detection","volume":"10","author":"Menotti","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Lu, Y.D., Yang, S.T., and Lai, S.H. (2022, January 18\u201324). Patchnet: A simple face anti-spoofing framework via fine-grained patch recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01964"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1109\/TIFS.2022.3183398","article-title":"Conv-MLP: A convolution and MLP mixed model for multimodal face anti-spoofing","volume":"17","author":"Wang","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_20","unstructured":"Park, J. (2018). Bam: Bottleneck attention module. arXiv."},{"key":"ref_21","unstructured":"Dosovitskiy, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"George, A., and Marcel, S. (2021, January 4\u20137). On the effectiveness of vision transformers for zero-shot face anti-spoofing. Proceedings of the IEEE International Joint Conference on Biometrics (IJCB), Shenzhen, China.","DOI":"10.1109\/IJCB52358.2021.9484333"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1109\/TIFS.2021.3055018","article-title":"Camera invariant feature learning for generalized face anti-spoofing","volume":"16","author":"Chen","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1109\/TIFS.2020.3013214","article-title":"Learning one class representations for face presentation attack detection using multi-channel convolutional neural networks","volume":"16","author":"George","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1109\/TIFS.2023.3235581","article-title":"Consistency regularization for deep face anti-spoofing","volume":"18","author":"Wang","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, X., and Yuan, Y. (2022, January 18\u201324). Sigma: Semantic-complete graph matching for domain adaptive object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00522"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1109\/TAI.2024.3365093","article-title":"CTRL: Clustering training losses for label error detection","volume":"5","author":"Yue","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dai, Z., Wang, G., Yuan, W., Zhu, S., and Tan, P. (2022, January 4\u20138). Cluster contrast for unsupervised person re-identification. Proceedings of the Asian Conference on Computer Vision, Macao, China.","DOI":"10.1007\/978-3-031-26351-4_20"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1007\/s11263-023-01778-x","article-title":"Adversarial learning domain-invariant conditional features for robust face anti-spoofing","volume":"131","author":"Jiang","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"ref_30","unstructured":"Li, C., Liu, X., Li, W., Wang, C., Liu, H., Liu, Y., Chen, Z., and Yuan, Y. (2024). U-kan makes strong backbone for medical image segmentation and generation. arXiv."},{"key":"ref_31","unstructured":"Jianwei, Y., Zhen, L., and Stan, L.Z. (2014). Learn convolutional neural network for face anti-spoofing. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, X., Liu, A., Zhao, C., Wan, J., Escalera, S., Shi, H., Wang, Z., and Li, S.Z. (2019, January 15\u201320). A dataset and benchmark for large-scale multi-modal face anti-spoofing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00101"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, A., Tan, Z., Wan, J., Li, X., Escalera, S., Guo, G., and Li, S.Z. (2021, January 5\u20139). Casia-surf cefa: A benchmark for multi-modal cross-ethnicity face anti-spoofing. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Virtual.","DOI":"10.1109\/WACV48630.2021.00122"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., and Hadid, A. (June, January 30). OULU-NPU: A mobile face presentation attack database with real-world variations. Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA.","DOI":"10.1109\/FG.2017.77"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhou, Z., and Guo, Z. (2021, January 10\u201315). Face anti-spoofing using spatial pyramid pooling. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412407"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, G., Lan, C., Han, H., Shan, S., and Chen, X. (2019, January 16\u201320). Multi-modal face presentation attack detection via spatial and channel attentions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00200"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/TIFS.2022.3158062","article-title":"Learning multi-granularity temporal characteristics for face anti-spoofing","volume":"17","author":"Wang","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kong, Y., Li, X., Hao, G., and Liu, C. (2022). Face anti-spoofing method based on residual network with channel attention mechanism. Electronics, 11.","DOI":"10.3390\/electronics11193056"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1049\/bme2.12002","article-title":"Cross-ethnicity face anti-spoofing recognition challenge: A review","volume":"10","author":"Liu","year":"2021","journal-title":"IET Biom."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yu, Z., Qin, Y., Li, X., Wang, Z., Zhao, C., Lei, Z., and Zhao, G. (2020, January 14\u201319). Multi-modal face anti-spoofing based on central difference networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00333"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1049\/iet-ipr.2018.5560","article-title":"Robust face anti-spoofing using CNN with LBP and WLD","volume":"13","author":"Khammari","year":"2019","journal-title":"IET Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Boulkenafet, Z., Komulainen, J., Akhtar, Z., Benlamoudi, A., Samai, D., Bekhouche, S.E., Ouafi, A., Dornaika, F., Taleb-Ahmed, A., and Qin, L. (2017, January 1\u20134). A competition on generalized software-based face presentation attack detection in mobile scenarios. Proceedings of the 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA.","DOI":"10.1109\/BTAS.2017.8272758"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lin HY, S., and Su, Y.W. (2019, January 2\u20134). Convolutional neural networks for face anti-spoofing and liveness detection. Proceedings of the 2019 6th International Conference on Systems and Informatics (ICSAI), Shanghai, China.","DOI":"10.1109\/ICSAI48974.2019.9010495"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Atoum, Y., Liu, Y., Jourabloo, A., and Liu, X. (2017, January 1\u20134). Face anti-spoofing using patch and depth-based CNNs. Proceedings of the 2017 IEEE International Joint Conference on Biometrics (IJCB), Denver, CO, USA.","DOI":"10.1109\/BTAS.2017.8272713"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, Y., Jourabloo, A., and Liu, X. (2018, January 18\u201322). Learning deep models for face anti-spoofing: Binary or auxiliary supervision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00048"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/978-3-030-01261-8_18","article-title":"Face de-spoofing: Anti-spoofing via noise modeling","volume":"Volume 11217","author":"Jourabloo","year":"2018","journal-title":"Lecture Notes in Computer"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"George, A., and Marcel, S. (2019, January 4\u20137). Deep pixel-wise binary supervision for face presentation attack detection. Proceedings of the 2019 International Conference on Biometrics (ICB), Crete, Greece.","DOI":"10.1109\/ICB45273.2019.8987370"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Chen, D., Liu, M., Dong, X., Yuan, L., and Liu, Z. (2022, January 18\u201324). Mobile-former: Bridging mobilenet and transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00520"},{"key":"ref_50","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning (PMLR), Long Beach, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_52","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Gradcam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/4\/116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:12:17Z","timestamp":1760029937000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/4\/116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,10]]},"references-count":53,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["jimaging11040116"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11040116","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,10]]}}}