Abstract: Abstract: Facial expression detection and classification is a challenging task in the field of human-computer interaction. In order to solve the problems of large parameters and low classification accuracy in current facial expression recognition models， a lightweight facial expression recognition method based on sandglass structure and attention mechanism is proposed. First， the improved sandglass structure is used to build a lightweight backbone feature extraction network. Then a novel feature fusion attention module is designed. Focus pooled features are fused to extract key details， and lightweight ECA attention mechanism is embedded to strengthen key expression features to improve the feature expression ability of the model. Finally， various training strategies such as Random Erasing and Dropout are adopted to alleviate the over fitting phenomenon of lightweight networks， so as to improve the generalization performance of the model. Testing experiments were conducted on two classical expression datasets FER2013 and CK+， and the recognition rates reached 71.72% and 95.96% respectively， while the number of parameters is only about 1×106.

Key words: Key words: expression recognition, sandglass, attention mechanism, lightweight