{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:59:15Z","timestamp":1772553555738,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T00:00:00Z","timestamp":1571011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012659","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61806016 and 61673033"],"award-info":[{"award-number":["No. 61806016 and 61673033"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,10,14]]},"DOI":"10.1145\/3340555.3353739","type":"proceedings-article","created":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T12:49:48Z","timestamp":1571316588000},"page":"40-48","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Continuous Emotion Recognition in Videos by Fusing Facial Expression, Head Pose and Eye Gaze"],"prefix":"10.1145","author":[{"given":"Suowei","family":"Wu","sequence":"first","affiliation":[{"name":"Beihang University, China"}]},{"given":"Zhengyin","family":"Du","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]},{"given":"Weixin","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]},{"given":"Di","family":"Huang","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]},{"given":"Yunhong","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University, China"}]}],"member":"320","published-online":{"date-parts":[[2019,10,14]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACII.2015.7344583"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988257.2988260"},{"key":"e_1_3_2_1_3_1","unstructured":"Shaojie Bai J\u00a0Zico Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271(2018).  Shaojie Bai J\u00a0Zico Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271(2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00019"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988257.2988264"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1071-5819(03)00018-1"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2808196.2811634"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00124"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133944.3133949"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74889-2_43"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9280.2007.02024.x"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-994-9_10"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15892-6_39"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2808196.2811641"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Matthias Holschneider Richard Kronland-Martinet Jean Morlet and Ph Tchamitchian. 1990. A real-time algorithm for signal analysis with the help of the wavelet transform. In Wavelets. 286\u2013297.  Matthias Holschneider Richard Kronland-Martinet Jean Morlet and Ph Tchamitchian. 1990. A real-time algorithm for signal analysis with the help of the wavelet transform. In Wavelets. 286\u2013297.","DOI":"10.1007\/978-3-642-75988-8_28"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Soheil Khorram Zakaria Aldeneh Dimitrios Dimitriadis Melvin McInnis and Emily\u00a0Mower Provost. 2017. Capturing long-term temporal dependencies with convolutional networks for continuous emotion recognition. arXiv preprint arXiv:1708.07050(2017).  Soheil Khorram Zakaria Aldeneh Dimitrios Dimitriadis Melvin McInnis and Emily\u00a0Mower Provost. 2017. Capturing long-term temporal dependencies with convolutional networks for continuous emotion recognition. arXiv preprint arXiv:1708.07050(2017).","DOI":"10.21437\/Interspeech.2017-548"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2016.7532431"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461920"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2016.2629282"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2388676.2388783"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3266302.3266316"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133944.3133953"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2013.6553805"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2366127"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988257.2988270"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10919-018-0276-5"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472669"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2017.107"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2388676.2388779"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2008-192"},{"key":"e_1_3_2_1_36_1","unstructured":"Matthew\u00a0D Zeiler. 2012. ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701(2012).  Matthew\u00a0D Zeiler. 2012. ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701(2012)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3266302.3266313"}],"event":{"name":"ICMI '19: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION","location":"Suzhou China","acronym":"ICMI '19"},"container-title":["2019 International Conference on Multimodal Interaction"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340555.3353739","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340555.3353739","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:13:28Z","timestamp":1750202008000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340555.3353739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,14]]},"references-count":37,"alternative-id":["10.1145\/3340555.3353739","10.1145\/3340555"],"URL":"https:\/\/doi.org\/10.1145\/3340555.3353739","relation":{},"subject":[],"published":{"date-parts":[[2019,10,14]]},"assertion":[{"value":"2019-10-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}