{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:36:27Z","timestamp":1773772587274,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T00:00:00Z","timestamp":1611705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB1303200"],"award-info":[{"award-number":["2017YFB1303200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Project for Frontier Leading Basic Technology of Jiangsu Province","award":["BK20192004"],"award-info":[{"award-number":["BK20192004"]}]},{"name":"Science and Technology Major Project of Anhui Province","award":["17030901034"],"award-info":[{"award-number":["17030901034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recognizing facial expression has attracted much more attention due to its broad range of applications in human\u2013computer interaction systems. Although facial representation is crucial to final recognition accuracy, traditional handcrafted representations only reflect shallow characteristics and it is uncertain whether the convolutional layer can extract better ones. In addition, the policy that weights are shared across a whole image is improper for structured face images. To overcome such limitations, a novel method based on patches of interest, the Patch Attention Layer (PAL) of embedding handcrafted features, is proposed to learn the local shallow facial features of each patch on face images. Firstly, a handcrafted feature, Gabor surface feature (GSF), is extracted by convolving the input face image with a set of predefined Gabor filters. Secondly, the generated feature is segmented as nonoverlapped patches that can capture local shallow features by the strategy of using different local patches with different filters. Then, the weighted shallow features are fed into the remaining convolutional layers to capture high-level features. Our method can be carried out directly on a static image without facial landmark information, and the preprocessing step is very simple. Experiments on four databases show that our method achieved very competitive performance (Extended Cohn\u2013Kanade database (CK+): 98.93%; Oulu-CASIA: 97.57%; Japanese Female Facial Expressions database (JAFFE): 93.38%; and RAF-DB: 86.8%) compared to other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s21030833","type":"journal-article","created":{"date-parts":[[2021,1,27]],"date-time":"2021-01-27T06:10:54Z","timestamp":1611727854000},"page":"833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Patch Attention Layer of Embedding Handcrafted Features in CNN for Facial Expression Recognition"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3407-2905","authenticated-orcid":false,"given":"Xingcan","family":"Liang","sequence":"first","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6951-5633","authenticated-orcid":false,"given":"Linsen","family":"Xu","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Anhui Province Key Laboratory of Biomimetic Sensing and Advanced Robot Technology, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfu","family":"Liu","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhipeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaoxin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,27]]},"reference":[{"key":"ref_1","first-page":"193","article-title":"Communication without words","volume":"6","author":"Mehrabian","year":"2008","journal-title":"Commun. 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