基于无监督提取表情时空特征的情感识别

Emotion Recognition Based on Unsupervised Extraction of Facial Expression Spatio-temporal Features

情感识别是解决智能教学系统中情感缺失问题的关键技术。针对识别时如何从视频中有效提取人脸表情时空特征的问题,提出一种采用堆叠卷积独立子空间分析模型进行无监督特征提取的识别方法,来对疑惑、愉快和厌倦3种学习中最常出现的情感进行识别。该方法检测视频中的人脸区域并进行规范化处理,采用堆叠卷积独立子空间分析模型从视频块中无监督地学习表情的时空特征,采用线性支持向量机进行分类。实验结果表明,相比使用人工特征的方法,该方法能够更有效地提取视频中人脸表情的时空特征,获得更高的识别率,同时符合实时性要求。

Emotion recognition is the key to solving the problem of the absence of emotional communication in intelligent tutoring systems.According to the problem of effective extraction of facial expression spatio-temporal features from videos for emotion recognition,a recognition method based on unsupervised feature extraction using stacked convolutional independent subspace analysis （ISA） model was proposed to recognize three emotions including puzzlement,delight and boredom that most often appear in learning.This method first detects face in video and normalizes it,then adopts stacked convolutional ISA model to learn （without supervision） facial expression spatio-temporal features from video blocks,finally uses linear SVM classifier to recognize different emotions.Experimental results indicate that this method can extract spatio-temporal expression features more effectively than the use of hand-designed features,as well as recognition rate is better,and it meets the requirement of real-time.