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Correlations across a batch of images in the individual path of the dual incoming paths are first extracted by a self attention mechanism in the batch dimension; these two paths are fused together to consolidate their information and then split again into two separate paths; the information along the channel dimension is extracted using a generalized form of channel attention, an adaptive graph channel attention, which provides each element of the incoming signal with a weight that is adapted to the incoming signal. The combination of these two paths, together with two skip connections from the input to the batch attention to the output of the adaptive channel attention, then passes into a residual network, with neighborhood attention to extract fine features in the spatial dimension. This novel dual path architecture has been shown experimentally to achieve a better balance between the competing demands in an SDAC problem than other competing approaches. Ablation studies enable the determination of the relative importance of these three attention mechanisms. Competitive results are obtained on two non-aligned face expression recognition datasets, RAF-DB and SFEW, when compared with other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s25123815","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:36:00Z","timestamp":1750239360000},"page":"3815","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dual-Branch Multi-Dimensional Attention Mechanism for Joint Facial Expression Detection and Classification"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4605-7534","authenticated-orcid":false,"given":"Cheng","family":"Peng","sequence":"first","affiliation":[{"name":"School of Computing, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4965-2901","authenticated-orcid":false,"given":"Bohao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6481-5458","authenticated-orcid":false,"given":"Kun","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Computing, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3581-9476","authenticated-orcid":false,"given":"Bowen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2583-0433","authenticated-orcid":false,"given":"Genan","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2904-7008","authenticated-orcid":false,"given":"Ah Chung","family":"Tsoi","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Saganowski, S. 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