Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification

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Institute of Electrical and Electronics Engineers Inc.

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info:eu-repo/semantics/closedAccess

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We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions. © 2017 IEEE.

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3dMD;Baidu;DI4D;et al.;Mitsubishi Electric Research Laboratories, Inc;NSF
12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 -- 30 May 2017 through 3 June 2017 -- -- 128713

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Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heterogeneous Face Recognition, HFR 2017, Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation, DCER and HPE 2017 and 3rd Facial Expression Recognition and Analysis Challenge, FERA 2017

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Loob, C., Rasti, P., Lusi, I., Junior, J. C. S. J., Baro, X., Escalera, S., Sapinski, T., ... 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). (May 01, 2017). Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification. 833-838.

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