期刊
NEUROCOMPUTING
卷 409, 期 -, 页码 341-350出版社
ELSEVIER
DOI: 10.1016/j.neucom.2020.05.081
关键词
Facial expression recognition; Convolutional neural network; Near-infrared; Correlation emotion label distribution; Human emotion detection
资金
- National Natural Science Foundation of China [61875068, 61873220, 61505064]
- National Key Research and Development Program of China [2017YFB1401300, 2017YFB1401303]
- Research Grants Council of Hong Kong [CityU 11205015, CityU 11255716]
- Fundamental Research Funds for the Central Universities [CCNU20ZT017, CCNU2020ZN008]
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. (C) 2020 Elsevier B.V. All rights reserved.
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