文献类型：期刊文献

英文题名：Recognition of emotions using multimodal physiological signals and an ensemble deep learning model

作者：Yin, Zhong[1];Zhao, Mengyuan[2];Wang, Yongxiong[1];Yang, Jingdong[1];Zhang, Jianhua[3]

机构：[1]Univ Shanghai Sci & Technol, Minist Educ, Engn Res Ctr Opt Instrument & Syst, Shanghai Key Lab Modern Opt Syst, Shanghai 200093, Peoples R China;[2]Univ Shanghai Sci & Technol, Sch Social Sci, Shanghai 200093, PR, Peoples R China;[3]East China Univ Sci & Technol, Dept Automat, Shanghai 200237, PR, Peoples R China

年份：2017

卷号：140

起止页码：93

外文期刊名：COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

收录：;EI(收录号：20165203175206);WOS:【SSCI(收录号:WOS:000397074300011)，SCI-EXPANDED(收录号:WOS:000397074300011)】；

基金：This work is supported by the National Natural Science Foundation of China under Grant No. 61673276, No. 11502145, No. 61603256, the Foundation of Shanghai Municipal Education Commission and the Faculty Innovation Ability Development Project of University of Shanghai for Science and Technology.

语种：英文

外文关键词：Emotion recognition; Affective computing; Physiological signals; Deep learning; Ensemble learning

摘要：Background and Objective: Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. Methods: In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. Results: DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. Conclusions: The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances. (C) 2016 Elsevier Ireland Ltd. All rights reserved.