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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T01:09:14Z","timestamp":1725152954364},"reference-count":73,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,4]],"date-time":"2020-02-04T00:00:00Z","timestamp":1580774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["SenseEmotion","e:Med, CONFIRM, ID 01ZX1708C"],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Science and Education Baden-W\u00fcrttemberg","award":["Projekt ZIV"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Several approaches have been proposed for the analysis of pain-related facial expressions. These approaches range from common classification architectures based on a set of carefully designed handcrafted features, to deep neural networks characterised by an autonomous extraction of relevant facial descriptors and simultaneous optimisation of a classification architecture. In the current work, an end-to-end approach based on attention networks for the analysis and recognition of pain-related facial expressions is proposed. The method combines both spatial and temporal aspects of facial expressions through a weighted aggregation of attention-based neural networks\u2019 outputs, based on sequences of Motion History Images (MHIs) and Optical Flow Images (OFIs). Each input stream is fed into a specific attention network consisting of a Convolutional Neural Network (CNN) coupled to a Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN). An attention mechanism generates a single weighted representation of each input stream (MHI sequence and OFI sequence), which is subsequently used to perform specific classification tasks. Simultaneously, a weighted aggregation of the classification scores specific to each input stream is performed to generate a final classification output. The assessment conducted on both the BioVid Heat Pain Database (Part A) and SenseEmotion Database points at the relevance of the proposed approach, as its classification performance is on par with state-of-the-art classification approaches proposed in the literature.<\/jats:p>","DOI":"10.3390\/s20030839","type":"journal-article","created":{"date-parts":[[2020,2,5]],"date-time":"2020-02-05T08:18:48Z","timestamp":1580890728000},"page":"839","source":"Crossref","is-referenced-by-count":26,"title":["Two-Stream Attention Network for Pain Recognition from Video Sequences"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6769-8410","authenticated-orcid":false,"given":"Patrick","family":"Thiam","sequence":"first","affiliation":[{"name":"Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany"},{"name":"Institute of Neural Information Processing, Ulm University, James-Frank-Ring, 89081 Ulm, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4759-5254","authenticated-orcid":false,"given":"Hans A.","family":"Kestler","sequence":"additional","affiliation":[{"name":"Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5118-0812","authenticated-orcid":false,"given":"Friedhelm","family":"Schwenker","sequence":"additional","affiliation":[{"name":"Institute of Neural Information Processing, Ulm University, James-Frank-Ring, 89081 Ulm, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00138-010-0298-4","article-title":"Motion History Image: its variants and applications","volume":"23","author":"Ahad","year":"2012","journal-title":"Mach. 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