HistNet: Histogram-based convolutional neural network with Chi-squared deep metric learning for facial expression recognition

Facial expression recognition is a challenging problem in machine learning. There is much research has been conducted in this field; however, the accuracy of facial expression recognition, especially in uncontrolled conditions, needs much improvement. In this paper, a deep histogram metric learning in a Convolutional Neural Network (CNN) is presented for facial expression recognition. The proposed CNN utilizes a histogram calculation layer to provide statistical description of feature maps at the output of the convolutional layers. To train the proposed CNN in histogram space, a learnable matrix (equivalent to the fully connected layer) is introduced in chi-squared distance equation. Then, the modified equation is used in the loss function. The recognition rates of the proposed CNN for seven-class facial expression recognition on four well-known databases including CK+, MMI, SFEW, and RAF-DB are 98.47%, 83.41%, 61.01%, and 89.28%, respectively. The results show superiority of the proposed CNN compared to the state-of-the-art methods. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a deep histogram metric learning approach based on a Convolutional Neural Network (CNN) for facial expression recognition. By introducing a histogram calculation layer and a learnable matrix, the accuracy is improved and the proposed CNN outperforms state-of-the-art methods on multiple databases.