Facial expression recognition with multi-scale convolution neural network

Abstract

We present a deep convolutional neural network (CNN) architecture for facial expression recognition. Inspired by the fact that regions located around certain facial parts (e.g. mouth, nose, eyes, and brows) contain the most representative information of expressions, an architecture extracts features at different scale from intermediate layers is designed to combine both local and global information. In addition, noticing that in specific to facial expression recognition, traditional face alignment would distort the images and lose expression information. To avoid this side effect, we apply batch normalization to the architecture instead of face alignment and feed the network with original images. Moreover, considering the tiny differences between classes caused by the same facial movements, a triplet-loss learning method is used to train the architecture, which improves the discrimination of deep features. Experiments show that the proposed architecture achieves superior performance to other state-of-the-art methods on the FER2013 dataset.