{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T05:36:47Z","timestamp":1772861807318,"version":"3.50.1"},"reference-count":36,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100007301","name":"Kunming University of Science and Technology","doi-asserted-by":"publisher","award":["2309\/CB22144S078A"],"award-info":[{"award-number":["2309\/CB22144S078A"]}],"id":[{"id":"10.13039\/501100007301","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007301","name":"Kunming University of Science and Technology","doi-asserted-by":"publisher","award":["NO.62266025"],"award-info":[{"award-number":["NO.62266025"]}],"id":[{"id":"10.13039\/501100007301","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Facial Expression Recognition (FER) has become more crucial in intelligent human-computer contact systems in recent years. The complexity and confusion of target emotions lead to low accuracy of FER. This study proposes an attentional residual network based spatial transformer mechanism for FER to establish an effective emotion recognition model. First, the learnable module Spatial Transformer Network (STN) was introduced to actively transform the feature map in order to learn the feature map's more general distortion invariance. Second, the parameters and structure of ResNet18 were adjusted, and it was connected to the STN in an end-to-end manner. Finally, by introducing the Squeeze and Excitation (SE) block, which replaces the ReLu function with the Mish function, we improved the stability and precision of the channel weight adjustment. The verification was carried out on three public datasets FER2013, CK+, and JAFFE. Among them, the FER2013 dataset is separated into three sections, training set, public validation set, and private validation set. The ten-fold cross-validation approach was used for the sparse CK\u2009+\u2009and JAFFE datasets. On the FER2013, CK+, and JAFFE datasets, accuracy rates of 73.25%, 99.18%, and 97.10% were attained, respectively.<\/jats:p>","DOI":"10.1177\/18758967251355732","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T03:06:58Z","timestamp":1751598418000},"page":"751-766","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Attentional Residual Network Based Spatial Transformer Mechanism for Facial Expression Recognition"],"prefix":"10.1177","volume":"49","author":[{"given":"Dangguo","family":"Shao","sequence":"first","affiliation":[{"name":"Yunnan Key Laboratory of Computer Technologies Application, Kunming, China"},{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming, China"}]},{"given":"Gaofei","family":"Gao","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming, China"}]}],"member":"179","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.16-23-07678.1996"},{"key":"e_1_3_3_3_1","unstructured":"Akamatsu M. J. S. L. Kamachi M. Gyoba J. Budynek J. (1998). The Japanese female facial expression (JAFFE) database. In: Proceedings of the third international conference on automatic face and gesture recognition Nara Japan 14\u201316 April pp. 14\u201316."},{"key":"e_1_3_3_4_1","unstructured":"Aouayeb M. Hamidouche W. Soladie C. Kpalma K. Seguier R. (2021). Learning vision transformer with squeeze and excitation for facial expression recognition. arXiv preprint arXiv:2107.03107."},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1192\/bjp.bp.106.028829"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/YCO.0b013e3283503669"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2016.06.020"},{"key":"e_1_3_3_8_1","doi-asserted-by":"crossref","unstructured":"Goodfellow I. J. Erhan D. Carrier P. L. Courville A. Mirza M. Hamner B. Cukierski W. Tang Y. Thaler D. Lee D. H. Zhou Y. (2013). Challenges in representation learning: A report on three machine learning contests. In: International conference on neural information processing. Springer Berlin Heidelberg.","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"e_1_3_3_9_1","doi-asserted-by":"crossref","unstructured":"He K. Zhang X. Ren S. Sun J. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/da.21888"},{"key":"e_1_3_3_11_1","doi-asserted-by":"crossref","unstructured":"Hu J. Shen L. Sun G. (2018). Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_3_12_1","article-title":"Spatial transformer networks","volume":"28","author":"Jaderberg M.","year":"2015","unstructured":"Jaderberg M., Simonyan K., Zisserman A. (2015). Spatial transformer networks. Advances in neural Information Processing Systems, 28.","journal-title":"Advances in neural Information Processing Systems"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.06.004"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2907327"},{"key":"e_1_3_3_15_1","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky A.","year":"2012","unstructured":"Krizhevsky A., Sutskever I., Hinton G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102658"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3108838"},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2022.930584"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104209"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.102723"},{"key":"e_1_3_3_21_1","doi-asserted-by":"crossref","unstructured":"Lucey P. Cohn J. F. Kanade T. Saragih J. Ambadar Z. Matthews I. (2010). The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In: 2010 Ieee computer society conference on computer vision and pattern recognition-workshops. IEEE.","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"e_1_3_3_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.03.082"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21093046"},{"key":"e_1_3_3_24_1","unstructured":"Misra D. (2019). Mish: A self regularized non-monotonic neural activation function. arXiv preprint arXiv:1908.08681 4.2: 10-48550."},{"key":"e_1_3_3_25_1","doi-asserted-by":"crossref","unstructured":"Park U. Kim M. Jang Y. Lee G. Kim K. Kim I. J. Choi J. (2021). Robot facial expression framework for enhancing empathy in human-robot interaction. In: 2021 30th IEEE international conference on robot & human interactive communication (RO-MAN). IEEE.","DOI":"10.1109\/RO-MAN50785.2021.9515533"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12046-022-01943-x"},{"key":"e_1_3_3_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.07.027"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01855-5"},{"key":"e_1_3_3_29_1","doi-asserted-by":"crossref","unstructured":"Shi Z. Tan Z. (2021). Expression recognition method based on attention neural network. In: 2021 33rd chinese control and decision conference (CCDC). IEEE.","DOI":"10.1109\/CCDC52312.2021.9601786"},{"key":"e_1_3_3_30_1","unstructured":"Simonyan K. Zisserman A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"e_1_3_3_31_1","doi-asserted-by":"crossref","unstructured":"Srinivas A. Lin T. Y. Parmar N. Shlens J. Abbeel P. Vaswani A. (2021). Bottleneck transformers for visual recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"e_1_3_3_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2764438"},{"key":"e_1_3_3_33_1","doi-asserted-by":"crossref","unstructured":"Woo S. Park J. Lee J. Y. Kweon I. S. (2018). Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3169159"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3210109"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3056098"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-212846"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/18758967251355732","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/18758967251355732","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/18758967251355732","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T19:14:40Z","timestamp":1757963680000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/18758967251355732"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,4]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1177\/18758967251355732"],"URL":"https:\/\/doi.org\/10.1177\/18758967251355732","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,4]]}}}