关键词: 人脸表情识别, 关键点属性表征, 注意力机制, 卷积神经网络, 学习特征图

Abstract: The change of facial expression is very subtle and usually manifested in the change of some local points and regions in the image. The existing facial expression recognition methods are difficult to capture the subtle changes of facial expression, which does not have robust to interference from non-expressive regions. To obtain an efficient feature representation to describe the changes of facial expression, a facial expression recognition method that integrating key point attributes and attention representation is proposed. Firstly, the key points in face image are extracted by the module with channel attention and spatial attention, which realizes the weight distribution of different dimensions and positions, effectively avoids the interference of non-expressive regions, and obtain the feature representation of the local key points in the image. Then, transformer module is introduced to learn the correlation between different key points and guide the network to build a more distinguishing feature representation, so as to achieve accurate recognition. Finally, the experimental results on CK+, JAFFE and FER2013 public datasets show that the recognition accuracy of the proposed algorithm is up to 99.22%, 96.57% and 73.37% respectively.

Key words: facial expression recognition, key point attributes representation, attention mechanism, convolutional neural network, learning feature map