Expression Recognition Based on Residual Attention Mechanism and Pyramid Convolution

With the widespread application of deep learning, facial expression recognition technology develops rapidly. However, how to extract multi-scale features and utilize key features efficiently is still a challenge for facial expression recognition network. To solve these problems, pyramid convolution is employed to extract multi-scale features effectively, and spatial channel attention mechanism is introduced to enhance the expression of key features. An expression recognition network based on residual attention mechanism and pyramidal convolution is constructed to improve the recognition accuracy. Multi-task convolutional neural network is utilized for face detection, face clipping and face alignment, and then the preprocessed images are sent to the feature extraction network. Meanwhile, the network is trained by combining Softmax Loss and the Center Loss to narrow the difference between the same expressions and enlarge the distance between different expressions. Experiments show that the accuracy of the proposed network on Fer2013 dataset and CK+ dataset is high, the number of network parameters is small and the proposed method is more suitable for the application of realistic scenarios of expression recognition. © 2022 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.