Micro-expressions are rapid, difficult to observe with the naked eye, facial expressions that can reflect real human inner emotions. Micro-expression recognition is still a great challenge due to the characteristics of very short duration and subtle changes (small amplitude of muscle contraction or diastole). Based on this, this paper proposes a 3D convolutional microexpression recognition method based on attention mechanism, which is a dual-stream structure that can effectively utilize the features of image sequence and optical flow sequence. More effective micro-expression features are extracted using Attention layer, Co-Attention layer to better solve the micro-expression recognition task. Adequate experiments are conducted on the dataset to verify that the model has better recognition results.
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