Efficient Recognition of Facial Expression with Lightweight Octave Convolutional Neural Network

Abstract

In various social activities, people usually focus on the other person’s facial expression, which is an important element in interpersonal communication. The facial expression typically reflects a person’s current mood and conveys emotional information. Perceiving the facial expressions of people in images through cameras has always been a popular research topic. Previous studies have classified facial expressions into different categories, such as happy, sad, fear, angry, calm, disgust and surprise, and have identified them using image processing methods. However, traditional image processing methods have a low detection efficiency and low recognition accuracy due to variation of perspectives. As a result, most of them can only be applied to the front face and short distance situations. In this paper, we propose a lightweight deep learning framework for facial expression recognition using Octave convolutional neural network (FerOctNet). FerOctNet including multi-scale convolutional processing and residual learning is able to obtain multi-scale features with enriched representation ability by integrating the deep level features with rich semantic information with shallow details of the features. Compared with other deep learning networks, not only does the proposed method have a good recognition rate, but also contains fewer parameters in the network.