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Accordingly, as a security guarantee to prevent the face authentication from being attacked, the study of face presentation attack detection is developed in this community. In this work, a face presentation attack detector is designed based on residual color texture representation (RCTR). Existing methods lack of effective data preprocessing, and we propose to adopt DW-filter for obtaining residual image, which can effectively improve the detection efficiency. Subsequently, powerful CM texture descriptor is introduced, which performs better than widely used descriptors such as LBP or LPQ. Additionally, representative texture features are extracted from not only RGB space but also more discriminative color spaces such as HSV, YCbCr, and CIE 1976 L\u2217a\u2217b (LAB). Meanwhile, the RCTR is fed into the well-designed classifier. Specifically, we compare and analyze the performance of advanced classifiers, among which an ensemble classifier based on a probabilistic voting decision is our optimal choice. Extensive experimental results empirically verify the proposed face presentation attack detector\u2019s superior performance both in the cases of intradataset and interdataset (mismatched training-testing samples) evaluation.<\/jats:p>","DOI":"10.1155\/2021\/6652727","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T21:50:22Z","timestamp":1615931422000},"page":"1-16","source":"Crossref","is-referenced-by-count":15,"title":["Towards Face Presentation Attack Detection Based on Residual Color Texture Representation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7910-2031","authenticated-orcid":true,"given":"Yuting","family":"Du","sequence":"first","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4912-2132","authenticated-orcid":true,"given":"Tong","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Zhengzhou Science and Technology Institute, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9332-5258","authenticated-orcid":true,"given":"Ming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3503-8167","authenticated-orcid":true,"given":"Ning","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2016.03.019"},{"key":"2","first-page":"1","article-title":"An original face anti-spoofing approach using partial convolutional neural network","author":"L. 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