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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Previous approaches usually learn spoofing features from a single perspective, in which only universal cues shared by all attack types are explored. However, such single-perspective-based approaches ignore the differences among various attacks and commonness between certain attacks and bona fides, thus tending to neglect some non-universal cues that contain strong discernibility against certain types. As a result, when dealing with multiple types of attacks, the above approaches may suffer from the uncomprehensive representation of bona fides and spoof faces. In this work, we propose a novel Advanced Multi-Perspective Feature Learning network (AMPFL), in which multiple perspectives are adopted to learn discriminative features, to improve the performance of FAS. Specifically, the proposed network first learns universal cues and several perspective-specific cues from multiple perspectives, then aggregates the above features and further enhances them to perform face anti-spoofing. In this way, AMPFL obtains features that are difficult to be captured by single-perspective-based methods and provides more comprehensive information on bona fides and spoof faces, thus achieving better performance for FAS. Experimental results show that our AMPFL achieves promising results in public databases, and it effectively solves the issues of single-perspective-based approaches.<\/jats:p>","DOI":"10.1145\/3575660","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T14:28:46Z","timestamp":1670509726000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving Face Anti-spoofing via Advanced Multi-perspective Feature Learning"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2230-5716","authenticated-orcid":false,"given":"Zhuming","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing University of Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3946-9987","authenticated-orcid":false,"given":"Yaowen","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing University of Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7209-0215","authenticated-orcid":false,"given":"Lifang","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing University of Technology, China and Beijing Key Laboratory of Computational Intelligence and Intelligent System, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6010-1792","authenticated-orcid":false,"given":"Hu","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, China and University of Chinese Academy of Sciences, China and Pengcheng Laboratory, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4419-4287","authenticated-orcid":false,"given":"Yukun","family":"Ma","sequence":"additional","affiliation":[{"name":"Henan Institute of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6100-2788","authenticated-orcid":false,"given":"Zun","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2016. 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