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A Novel Supervised Bimodal Emotion Recognition Approach Based on Facial Expression and Body Gesture
Jingjie YAN Guanming LU Xiaodong BAI Haibo LI Ning SUN Ruiyu LIANG
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2018/11/01
Online ISSN: 1745-1337
Type of Manuscript: LETTER
facial expression, body gesture, bimodal emotion recognition, feature fusion, supervised multiset canonical correlation analysis (SMCCA),
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In this letter, we propose a supervised bimodal emotion recognition approach based on two important human emotion modalities including facial expression and body gesture. A effectively supervised feature fusion algorithms named supervised multiset canonical correlation analysis (SMCCA) is presented to established the linear connection between three sets of matrices, which contain the feature matrix of two modalities and their concurrent category matrix. The test results in the bimodal emotion recognition of the FABO database show that the SMCCA algorithm can get better or considerable efficiency than unsupervised feature fusion algorithm covering canonical correlation analysis (CCA), sparse canonical correlation analysis (SCCA), multiset canonical correlation analysis (MCCA) and so on.