Three convolutional neural network models for facial expression recognition in the wild

Abstract

Facial expression recognition (FER) in the wild is a novel and challenging topic in the field of human emotion perception. Different kinds of convolutional neural network (CNN) approaches have been applied to this topic, but few of them ever considered what kind of architecture was better for the FER research. In this paper, we proposed three novel CNN models with different architectures. The first one is a shallow network, named the Light-CNN, which is a fully convolutional neural network consisting of six depthwise separable residual convolution modules to solve the problem of complex topology and over-fitting. The second one is a dual-branch CNN which extracts traditional LBP features and deep learning features in parallel. The third one is a pre-trained CNN which is designed by transfer learning technique to overcome the shortage of training samples. Extensive evaluations on three popular datasets (public CK+, multi-view BU-3DEF and FER2013 datasets) demonstrated that our models were competitive and representative in the field of FER in the wild research. We achieved significant better results with comparisons to plenty of state-of-the-art approaches. Moreover, we provided discussions on the effectiveness and practicability of CNNs with different feature types and architectures for FER in the wild as well.