{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:50:38Z","timestamp":1774417838643,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,10]],"date-time":"2019-03-10T00:00:00Z","timestamp":1552176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Face liveness detection is important for ensuring security. However, because faces are shown in photographs or on a display, it is difficult to detect the real face using the features of the face shape. In this paper, we propose a thermal face-convolutional neural network (Thermal Face-CNN) that knows the external knowledge regarding the fact that the real face temperature of the real person is 36~37 degrees on average. First, we compared the red, green, and blue (RGB) image with the thermal image to identify the data suitable for face liveness detection using a multi-layer neural network (MLP), convolutional neural network (CNN), and C-support vector machine (C-SVM). Next, we compared the performance of the algorithms and the newly proposed Thermal Face-CNN in a thermal image dataset. The experiment results show that the thermal image is more suitable than the RGB image for face liveness detection. Further, we also found that Thermal Face-CNN performs better than CNN, MLP, and C-SVM when the precision is slightly more crucial than recall through F-measure.<\/jats:p>","DOI":"10.3390\/sym11030360","type":"journal-article","created":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T03:49:31Z","timestamp":1552362571000},"page":"360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Face Liveness Detection Using Thermal Face-CNN with External Knowledge"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7376-3525","authenticated-orcid":false,"given":"Jongwoo","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea"}]},{"given":"In-Jeong","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong Campus, Sejong City 30019, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0169-7439(97)00061-0","article-title":"Introduction to multi-layer feed-forward neural networks","volume":"39","author":"Svozil","year":"1997","journal-title":"Chemom. 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