Facial Expression Recognition Based on Deep Binary Convolutional Network

Abstract

Facial expression recognition (FER) is a challenging task because of the large number of parameters, low speed and insufficient feature representation of the expression region. In order to address the above-mentioned challenges, a deep binary convolutional network for FER is proposed. Firstly, a lightweight convolution network (BRNet) with parallel operation of binary convolution and traditional convolution is designed to reduce the complexity of network and improve the speed of recognition. Secondly, a dynamic radius strategy is constructed to extract binary features and form the attention weight of expression region so that the local features and global features can be fused effectively. Finally, the cross-entropy and <italic>L</italic>₂ loss are designed for quick and accurate expression classification. Experiments show that the average accuracy of the proposed method is 99.25% and 93.85% on CK+ and Oulu-CASIA respectively, which are higher than the other similar lightweight convolutional networks. The parameters and computation cost of network are 5.0×10⁵ bytes and 2.1×10⁶ bytes. In contrast, the computation cost of EfficientFace is about 77 times than proposed method. As a result, the effectiveness and lightness of proposed method have been proved.