{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:56:03Z","timestamp":1780502163061,"version":"3.54.1"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"vor","delay-in-days":242,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>A face\u2010based authentication system has become an important topic in various fields of IoT applications such as identity validation for social care, crime detection, ATM access, computer security, etc. However, these authentication systems are vulnerable to different attacks. Presentation attacks have become a clear threat for facial biometric\u2010based authentication and security applications. To address this issue, we proposed a deep learning approach for face spoofing detection systems in IoT cloud\u2010based environment. The deep learning approach extracted features from multicolor space to obtain more information from the input face image regarding luminance and chrominance data. These features are combined and selected by the Minimum Redundancy Maximum Relevance (mRMR) algorithm to provide an efficient and discriminate feature set. Finally, the extracted deep color\u2010based features of the face image are used for face spoofing detection in a cloud environment. The proposed method achieves stable results with less training data compared to conventional deep learning methods. This advantage of the proposed approach reduces the time of processing in the training phase and optimizes resource management in storing training data on the cloud. The proposed system was tested and evaluated based on two challenging public access face spoofing databases, namely, Replay\u2010Attack and ROSE\u2010Youtu. The experimental results based on these databases showed that the proposed method achieved satisfactory results compared to the state\u2010of\u2010the\u2010art methods based on an equal error rate (EER) of 0.2% and 3.8%, respectively, for the Replay\u2010Attack and ROSE\u2010Youtu databases.<\/jats:p>","DOI":"10.1155\/2021\/5047808","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T17:56:46Z","timestamp":1630432606000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["IoT Cloud\u2010Based Framework for Face Spoofing Detection with Deep Multicolor Feature Learning Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7923-5253","authenticated-orcid":false,"given":"Sajad","family":"Einy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9742-6021","authenticated-orcid":false,"given":"Cemil","family":"Oz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6486-7223","authenticated-orcid":false,"given":"Yahya Dorostkar","family":"Navaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.02.040"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.01.024"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103277"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2017.07.001"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2018.12.008"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.01.050"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.08.019"},{"key":"e_1_2_8_8_2","doi-asserted-by":"crossref","unstructured":"PanG. 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