Recent years, the related research on facial expression recognition is increasingly attracting people’s attention. As an important branch of artificial intelligence, facial expression recognition has a significant role in human-computer interaction and other related fields. However, due to the high similarity between different expressions and the small interclass differences, it is still a daunting task for researchers to obtain a robust and accurate expression recognition model. Considering the characteristics of human expressions, a new convolution mechanism is proposed, which we call the attention convolution module. We also apply the convolution module to the field of expression recognition and propose a new expression recognition network (ATB-NET). The experimental results show that our method can focus on relatively important regions in the face, besides, compared with previous work, it also has a significant improvement on multiple public data sets (such as FER2013, CK+).
In human communication, besides direct verbal speech, facial expressions are also the main method to convey inner thoughts and emotions. By analyzing facial expressions, it is possible to obtain information about human inner emotions. Applying expression analysis algorithms in social robots can help robots accurately understand users' emotions and intentions, which in turn leads to better human-computer interaction.Therefore, in this paper, an effective & efficient expression recognition method is designed. The network uses the Ghost Module as the core module, and the lightweight attention module is used to increase the accuracy of expression recognition. In addition, the network is trained with a distributed label loss in order to solve the problems of insufficient data and long-tail of the facial expression dataset. Experiments prove that the network is superior, performs well on the RAF-DB dataset, with 86.8% accuracy without pretraining and has a fast processing speed. We also designed a real-time expression analysis system with this network as the backbone to simulate the actual working scenario of the robot, and found superior results to satisfy the functions of real-time human-computer interaction and efficient expression recognition.
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