Eyebrow emotional expression recognition using surface EMG signals
Eyebrow emotional expression recognition using surface EMG signals
复制标题
利用表面肌电信号识别眉毛情绪表情
DOI：
10.1016/j.neucom.2015.05.037
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发表时间：
2015-11-30
期刊：
影响因子：
6
通讯作者：
Wang, Jiangping
中科院分区：
文献类型：
--
作者：
Chen, Yumiao;Yang, Zhongliang;Wang, Jiangping
本研究的主要目的是在人机交互中有效地识别人脸表情。提出了一种基于表面肌电信号的眉毛表情识别方法。使用一个特别设计的头带，我们进行了一项实验，在该实验中，我们记录了来自额肌和皱眉肌的sEMG信号的六名参与者，他们被指示摆出愤怒，恐惧，悲伤，惊讶和厌恶的面部表情。随后，6个特征的sEMG时域提取和作为输入向量的情感识别模型的基础上的Elman神经网络（ENN）。该模型的性能进行了比较，另一种基于反向传播神经网络（BPNN）的识别模型。基于ENN的模型对五种情绪的平均识别率在训练集中为97.12%，在测试集中为96.12%，略上级基于BPNN的模型。(C)2015 Elsevier B.V.版权所有。
The main objective of this study is to recognize facial emotional expression effectively in human-computer interaction. A surface electromyography (sEMG) based eyebrow emotional expression recognition method is proposed. Using a specially designed headband, we conducted an experiment in which we recorded the sEMG signals from the frontalis and corrugator supercilii muscles of six participants who were instructed to pose the facial expressions of anger, fear, sadness, surprise and disgust. Subsequently, six features of the sEMG time domain were extracted and used as input vectors to an emotion recognition model based on an Elman neural network (ENN). The performance of this model was compared to another recognition model based on a Back Propagation neural network (BPNN). The average recognition rate for the five emotions achieved by the ENN-based model was 97.12% in the training and 96.12% in the test set, which was slightly superior to the performance of the BPNN-based model. (C) 2015 Elsevier B.V. All rights reserved.