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DOI码：10.1109/SMARTCOMP.2014.7043849

发表刊物：2014 International Conference on Smart Computing (SMARTCOMP)

关键字：Decoding, Image reconstruction, Vectors, Integrated circuits, Approximation methods

摘要：Facial expression recognition has important practical applications. In this paper, we propose a method based on the combination of optical flow and a deep neural network - stacked sparse autoencoder (SAE). This method classifies facial expressions into six categories (i.e. happiness, sadness, anger, fear, disgust and surprise). In order to extract the representation of facial expressions, we choose the optical flow method because it could analyze video image sequences effectively and reduce the influence of personal appearance difference on facial expression recognition. Then, we train the stacked SAE with the optical flow field as the input to extract high-level features. To achieve classification, we apply a softmax classifier on the top layer of the stacked SAE. This method is applied to the Extended Cohn-Kanade Dataset (CK+). The expression classification result shows that the SAE performances the classification effectively and successfully. Further experiments (transformation and purification) are carried out to illustrate the application of the feature extraction and input reconstruction ability of SAE.

合写作者：Xueshi Hou,陈健生, Chang Yang, Guangda Su, Weibei Dou,Weibei Dou, Yinyang Wang,Yinyang Wang, Tao Jiang, Jean-Marc Constans

第一作者：Yunfan Liu,Xue Wang

论文类型：会议论文

通讯作者：陈健生,Weibei Dou

页面范围：125-130

是否译文：否

发表时间：2014-11-03