关键词: 表情识别, Inception-w模型, Res-Bi-LSTM, 时空特征

Abstract:

Facial expression recognition has widely application prospects in human-computer interaction, such as clinical medicine, safe driving. To solve the problem of low accuracy due to the inability of the LSTM network to effectively extract correlative information prior and post frames, the framework is proposed based on Bi-LSTM, while the theory of identity mapping residual is used to effectively prevent the gradient disappearance caused by network depth. Because the Inception-V3 network has too many parameters and easy over-fitting, proposed to add two Reduction modules to reduce the parameters, and then get the more generalized model of Inception-w. Finally, the designed model has been evaluated on two datasets, CK+ and Oulu-CASIA, and compared with state-of-the-art methods. The highest accuracy reaches 99.6%, which indicates that the proposed method has better recognition accuracy within a certain range.

Key words: facial expression recognition, Inception-w model, Res-Bi-LSTM, spatio-temporal features