{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T06:16:59Z","timestamp":1764829019356,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62176084","62176083","PA2021GDSK0093","PA2022GDSK0068"],"award-info":[{"award-number":["62176084","62176083","PA2021GDSK0093","PA2022GDSK0068"]}]},{"name":"Fundamental Research Funds for the Central Universities of China","award":["62176084","62176083","PA2021GDSK0093","PA2022GDSK0068"],"award-info":[{"award-number":["62176084","62176083","PA2021GDSK0093","PA2022GDSK0068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Existing facial expression recognition methods have some drawbacks. For example, it becomes difficult for network learning on cross-dataset facial expressions, multi-region learning on an image did not extract the overall image information, and a frequency multiplication network did not take into account the inter-class and intra-class features in image classification. In order to deal with the above problems, in our current research, we raise a symmetric mode to extract the inter-class features and intra-class diversity features, and then propose a triple-structure network model based upon MobileNet V1, which is trained via a new multi-branch loss function. Such a proposed network consists of triple structures, viz., a global branch network, an attention mechanism branch network, and a diversified feature learning branch network. To begin with, the global branch network is used to extract the global features of the facial expression images. Furthermore, an attention mechanism branch network concentrates to extract inter-class features. In addition, the diversified feature learning branch network is utilized to extract intra-class diverse features. The network training is performed by using multiple loss functions to decrease intra-class differences and inter-class similarities. Finally, through ablation experiments and visualization, the intrinsic mechanism of our triple-structure network model is proved to be very reasonable. Experiments on the KDEF, MMI, and CK+ datasets show that the accuracy of facial expression recognition using the proposed model is 1.224%, 13.051%, and 3.085% higher than that using MC-loss (VGG16), respectively. In addition, related comparison tests and analyses proved that our raised triple-structure network model reaches better performance than dozens of state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/sym14102055","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T03:32:56Z","timestamp":1665459176000},"page":"2055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Triple-Structure Network Model Based upon MobileNet V1 and Multi-Loss Function for Facial Expression Recognition"],"prefix":"10.3390","volume":"14","author":[{"given":"Baojin","family":"Han","sequence":"first","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei University of Technology, Hefei 230601, China"},{"name":"School of Computer and Information, Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2122-0240","authenticated-orcid":false,"given":"Min","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei University of Technology, Hefei 230601, China"},{"name":"School of Computer and Information, Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei 230601, China"}]},{"given":"Xiaohua","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, Hefei University of Technology, Hefei 230601, China"},{"name":"School of Computer and Information, Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4860-9184","authenticated-orcid":false,"given":"Fuji","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei 230601, China"},{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1109\/TPAMI.2020.3017456","article-title":"Incomplete label multiple instance multiple label learning","volume":"44","author":"Nguyen","year":"2022","journal-title":"IEEE Trans. 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