Abstract:Facial emotions are the most intuitive way to react to changes in inner emotions. We propose a facial emotion recognition method that combines auxiliary classifiers(Acs) and multi-scale CBAM(MCBAM) by improving the Xception network model. And we design a lightweight network model AMDCNN. We introduce Acs in the middle layers of the model. The features extracted from the middle layer portion of the model are utilized to aid in emotion recognition. Finally, the recognition results of the Acs and the main classif… Show more
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