A Novel Facial Expression Recognition Scheme Based on Deep Neural Networks

Abstract

Automated emotion recognition in the wild from facial images remains a challenging problem. Although recent advances in Deep Learning have supposed a significant breakthrough in this topic, strong changes in pose, orientation and point of view severely harm current approaches. With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. In this paper, we propose a Low-rank Multimodal Fusion (LMF) method, which performs multimodal fusion using low-rank tensors to improve efficiency. Experimental results performed on emotion datasets demonstrate that our proposed method outperforms state-of-the-art.