Multilayer Convolution Sparse Coding for Expression Recognition

Abstract

Facial expression recognition is widely used in various research fields. For facial expression recognition problems, deep neural network methods have a complex structure and poor interpretability, while traditional machine learning methods have less plentiful diverse features and low recognition rates. Therefore, a new Multilayer Convolution Sparse Coding (MCSC) method is proposed for facial expression recognition. The MCSC method deeply extracts the salient features of the human face through a convolutional neural network. Furthermore, it uses a multilayer sparse coding to learn layer by layer to recognize different facial expression features based on sparse coding, which improves the recognition accuracy of facial expressions. Finally, the MCSC method was validated on three public facial expression datasets, i.e. JAFFE, CK +, and Fer2013. We also compared and analyzed 5 feature extraction approaches. The results show that MCSC has the best facial expression recognition performance in the comparison algorithm. Its accuracies of the three data sets reach to 90.8%, 98.2%, and 72.4%, respectively.