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Abstract. In this paper, we propose a facial expression recognition method using Support Vector Machine on mapped coordinate sequence features. The proposed system is almost fully automatic, in which landmark initialization is based on general knowledge with edge information, and missing information compensation is done by ASM. The geometric features of facial expressions were extracted from sequences of facial landmarks. Validation experiments were conducted using facial expression sequences extracted from a video based facial expression database.

This paper proposed a model pruning method based on local binary convolution (LBC) and squeeze-and-excitation (SE) optimization weights. We first proposed an efficient deep separation convolution model based on the LBC kernel. By expanding the number of LBC kernels in the model, we have trained a larger model with better results, but more parameters and slower calculation speed. Then, we extract the SE optimization weight value of each SE module according to the data samples and score the LBC kernel accordingly. Based on the score of each LBC kernel corresponding to the convolution channel, we performed channel-based model pruning, which greatly reduced the number of model parameters and accelerated the calculation speed. The model pruning method proposed in this paper is verified in the image classification database. Experiments show that, in the model using the LBC kernel, as the number of LBC kernels increases, the recognition accuracy will increase. At the same time, the experiment also proved that the recognition accuracy is maintained at a similar level in the small parameter model after channel-based model pruning by the SE optimization weight value.