{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:21:23Z","timestamp":1750220483642,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,22]]},"DOI":"10.1145\/3501409.3501605","type":"proceedings-article","created":{"date-parts":[[2022,1,2]],"date-time":"2022-01-02T06:18:12Z","timestamp":1641104292000},"page":"1106-1111","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Self-Adaptive and Multiple Activation Function Neural Network for Facial Expression Recognition"],"prefix":"10.1145","author":[{"given":"Shizhuo","family":"Zhang","sequence":"first","affiliation":[{"name":"Automation School, Beijing Information Science and Technology University, Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Liu","sequence":"additional","affiliation":[{"name":"Automation School, Beijing Information Science and Technology University, Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xibao","family":"Wu","sequence":"additional","affiliation":[{"name":"Automation School, Beijing Information Science and Technology University, Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbai","family":"Chen","sequence":"additional","affiliation":[{"name":"Automation School, Beijing Information Science and Technology University, Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,12,31]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1016\/j.heliyon.2018.e00938"},{"key":"e_1_3_2_1_2_1","volume-title":"Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055","author":"Zou Z","year":"2019","unstructured":"Zou Z, Shi Z, Guo Y, and Ye J. (2019). Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055."},{"key":"e_1_3_2_1_3_1","volume-title":"Review on deep learning techniques for marine object recognition: Architectures and algorithms. Control Engineering Practice, 104458","author":"Wang N","year":"2020","unstructured":"Wang N, Wang Y, and Er M.J. (2020). Review on deep learning techniques for marine object recognition: Architectures and algorithms. Control Engineering Practice, 104458."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1111\/j.1467-9280.2008.02116.x"},{"key":"e_1_3_2_1_5_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan K","year":"2014","unstructured":"Simonyan K, and Zisserman A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"key":"e_1_3_2_1_6_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097--1105","author":"Krizhevsky A","year":"2012","unstructured":"Krizhevsky A, Sutskever I, and Hinton G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097--1105."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.1109\/CVPR.2017.243"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"e_1_3_2_1_10_1","volume-title":"Feratt: Facial expression recognition with attention net. arXiv preprint arXiv:1902.03284, 3","author":"Fernandez P.D.M","year":"2019","unstructured":"Fernandez P.D.M, Pena F.A.G, Ren T.I, and Cunha A. (2019). Feratt: Facial expression recognition with attention net. arXiv preprint arXiv:1902.03284, 3."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.1109\/CVPR42600.2020.00693"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"e_1_3_2_1_13_1","volume-title":"Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375","author":"Agarap A.F.","year":"2018","unstructured":"Agarap A.F. (2018). Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_14_1","DOI":"10.1109\/CVPR46437.2021.00794"},{"doi-asserted-by":"publisher","unstructured":"Wu J.L Zhang L Liu S.L Jia K.P Zhu Z.W Fu X etc. (2021). Effect of Electroacupuncture on Learning and Memory Amino Acid Neurotransmitters and Their Receptors in the Hippocampus of Chronic Sleep Deprivation Rat. Shanghai Journal of Acupuncture and Moxibustion doi:10.13460\/j.issn.1005-0957.2021.13.2004","key":"e_1_3_2_1_15_1","DOI":"10.13460\/j.issn.1005-0957.2021.13.2004"},{"key":"e_1_3_2_1_16_1","first-page":"562","article-title":"Effect of pre-electroacupuncture regulating cholinergic nerve-related protein on learning and memory ability and brain inflammation in AD-like rats","volume":"2021","author":"Huang C. S","year":"2021","unstructured":"Huang C. S, He C, Chen H.R, Yu C.C, Wang X.S, Jiang T, Lu W, and Sun L.H. (2021). Effect of pre-electroacupuncture regulating cholinergic nerve-related protein on learning and memory ability and brain inflammation in AD-like rats. Chinese Journal of Gerontology, 2021, 41(03):562--567.","journal-title":"Chinese Journal of Gerontology"},{"volume-title":"2015 Principles of Neurobiology","author":"Luo L.Q","unstructured":"Luo L.Q, 2015 Principles of Neurobiology. Higher Education Press, Beijing, China.","key":"e_1_3_2_1_17_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1126\/science.167.3926.1745"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1080\/00221309.1965.9710688"},{"unstructured":"Paszke A Gross S Massa F Lerer A Bradbury J Chanan G etc. (2019). Pytorch: An imperative style high-performance deep learning library. Advances in neural information processing systems (32 8026--8037).","key":"e_1_3_2_1_20_1"},{"key":"e_1_3_2_1_21_1","volume-title":"Deep facial expression recognition: A survey","author":"Li S","year":"2020","unstructured":"Li S, and Deng W. (2020). Deep facial expression recognition: A survey. IEEE transactions on affective computing."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.1109\/TAFFC.2017.2740923"}],"event":{"acronym":"EITCE 2021","name":"EITCE 2021: 2021 5th International Conference on Electronic Information Technology and Computer Engineering","location":"Xiamen China"},"container-title":["Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3501409.3501605","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3501409.3501605","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:49:02Z","timestamp":1750193342000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3501409.3501605"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,22]]},"references-count":22,"alternative-id":["10.1145\/3501409.3501605","10.1145\/3501409"],"URL":"https:\/\/doi.org\/10.1145\/3501409.3501605","relation":{},"subject":[],"published":{"date-parts":[[2021,10,22]]},"assertion":[{"value":"2021-12-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}