Facial expression recognition method based on PSA—YOLO network - Ma, Ruoling; Zhang, Ruoyuan (Frontiers in Neurorobotics, 2023)
In order to improve the accuracy of facial expression facial expression recognition and speed up the processing speed, a facial expression recognition method based on PSA - YOLO (Pyramids Squeeze Attention - You only Look Once)was proposed. Based on CSPDarknet53, Focus structure and pyramid-compressed channel attention mechanism are integrated, and the network depth reduction strategy is adopted to construct PSA - CspDARKNet - 1 lightweight backbone network with small number of parameters and high accuracy. Secondly, in the neck of the network, a spatial pyramid convolution pooling module is built to enhance the spatial information extraction ability of the deep feature map with minimal computational cost, and the α - CIoU loss function is used as the bounding box loss function to improve the recognition accuracy of the target under high IoU (Intersection Over Union) threshold. The proposed method is validated on JAFFE, CK+ and Cohn - Kanade datasets. The experimental results show that the recognition accuracy of the proposed method is 83.84%, 85.09% and 84.87%, respectively, and the running time is less than 200 ms, which is better than the comparison method and has better applicability.