Novel multi-scale deep residual attention network for facial expression recognition

Abstract

Recently, the deep convolutional neural networks (CNNs) have shown great success and for facial expression recognition (FER). These CNN-based approaches have made breakthroughs in the accuracy by using deeper networks. As the amount of train data is very less, these models easily fall into the overfitting in the training. In this study, a novel multi-scale deep residual attention network (Ms-RAN) is proposed for FER. The proposed Ms-RAN is mainly based on the multi-scale residual attention unit, which consists of two different scale sub-units. Each sub-unit is composed of the convolutional layers, parametric rectified linear units (PReLUs), and the residual attention connection. By focusing on the relationship between channels and automatically learning the importance of different channel features, the proposed Ms-RAN can make the proposed model pay more attention to the most informative channel features, while suppressing those unimportant channel features. Owing to the unique design of Ms-RAN, the combination of various levels features can be enhanced in the proposed method, and valuable and different ranges of expressive information can also be provided for recognition. The experimental results demonstrate that the proposed method achieves superior performance than other state-of-the-art approaches on five databases, CK+, Oulu-CASIA, BU-3DFE, BP4D+, and MMI.

Keywords

• face recognition
• learning (artificial intelligence)
• feature extraction
• image classification
• emotion recognition
• neural nets
• different scale sub-units
• parametric rectified linear units
• residual attention connection
• different channel features
• ms-ran
• informative channel features
• exiting network
• multiscale structure
• residual attention mechanism
• original facial expression image
• novel multiscale deep residual attention network
• facial expression recognition
• deep convolutional neural networks
• deeper networks
• multiscale residual attention unit