Infrared facial expression recognition via Gaussian-based label distribution learning in the dark illumination environment for human emotion detection

• Published in: Neurocomputing (Amsterdam) 2020-10, Vol.409, p.341-350
• Main Authors: Zhang, Zhaoli, Lai, Chenghang, Liu, Hai, Li, You-Fu
• Format: Article
• Language: English
• Subjects: Convolutional neural network; Correlation emotion label distribution; Facial expression recognition; Human emotion detection; Near-infrared
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2020.05.081

•The natural correlation ambiguity is revealed, and a novel label distribution is constructed.•An end-to-end learning framework of FER is proposed in both feature learning and classifier learning.•Experimental results demonstrate that the proposed model achieves the best performance. Facial expression recognition task as a crucial step for emotion recognition remains an open challenge that due to individual expression correlation/ambiguity. In this paper, to tackle these challenges, a novel model with the correlation emotion label distribution learning is proposed for near-infrared (NIR) facial expression recognition which associates multiple emotions with each expression depend on the similarity of expressions. Firstly, the similarities of the seven basic expressions are calculated, and then guide the correlation emotion label distribution by predicting the latent label probability distribution of the expression. Furthermore, the proposed model can be learned in an end-to-end manner via a constructed convolutional neural network to classify the six basic facial expressions. Experimental results on Oulu_CASIA database demonstrate that the proposed method has achieved the superior performance on NIR expression recognition.