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Internet Things"],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>Mobile devices usually suffer from limited computation and storage resources, which seriously hinders them from deep neural network applications. In this article, we introduce a deeply tensor-compressed long short-term memory (LSTM) neural network for fast video-based facial expression recognition on mobile devices. First, a spatio-temporal facial expression recognition LSTM model is built by extracting time-series feature maps from facial clips. The LSTM-based spatio-temporal model is further deeply compressed by means of quantization and tensorization for mobile device implementation. Based on datasets of Extended Cohn-Kanade (CK+), MMI, and Acted Facial Expression in Wild 7.0, experimental results show that the proposed method achieves 97.96%, 97.33%, and 55.60% classification accuracy and significantly compresses the size of network model up to 221\u00d7 with reduced training time per epoch by 60%. Our work is further implemented on the RK3399Pro mobile device with a Neural Process Engine. The latency of the feature extractor and LSTM predictor can be reduced 30.20\u00d7 and 6.62\u00d7 , respectively, on board with the leveraged compression methods. Furthermore, the spatio-temporal model costs only 57.19 MB of DRAM and 5.67W of power when running on the board.<\/jats:p>","DOI":"10.1145\/3464941","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T10:07:37Z","timestamp":1626343657000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Fast Video Facial Expression Recognition by a Deeply Tensor-Compressed LSTM Neural Network for Mobile Devices"],"prefix":"10.1145","volume":"2","author":[{"given":"Peining","family":"Zhen","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Hai-Bao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Yuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Zhigang","family":"Ji","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology"}]},{"given":"Hao","family":"Yu","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology"}]}],"member":"320","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"volume-title":"n.d.RK3399Pro. 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