Facial expression recognition via region-based convolutional fusion network

摘要

One of the key challenge issues of deep-learning-based facial expression recognition (FER) is learning effective and robust features from variant samples. In this paper, Region-based Convolutional Fusion Network (RCFN) is proposed to solve this issue via three aspects. Firstly, a muscle movement model is built to segment out crucial regions of frontal face, providing well-unified patches with benefits of removing unrepresentative regions and greatly reducing interference caused by facial organs with varied sizes and positions among individuals. Secondly, a fast and practical network is constructed to extract robust triple-level features from low level to semantic level in each crucial region and fuse them for FER. Thirdly, constrained punitive loss is introduced to leverage the network training for boosting up FER performance. The experiment results show that RCFN is effective in commonly used datasets like KDEF, CK+, and Oulu-CASIA, and can achieve comparable performance with other state-of-the-art FER methods. (C) 2019 Elsevier Inc. All rights reserved.