Dominant and Complementary Emotion Recognition From Still Images of Faces

Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g.,...

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Bibliographic Details
Published in:	IEEE access Vol. 6; pp. 26391 - 26403
Main Authors:	Jianzhu Guo, Zhen Lei, Jun Wan, Avots, Egils, Hajarolasvadi, Noushin, Knyazev, Boris, Kuharenko, Artem, Silveira Jacques Junior, Julio C., Baró, Xavier, Demirel, Hasan, Escalera, Sergio, Allik, Jüri, Anbarjafari, Gholamreza
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01-01-2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) IEEE Access
Subjects:	Affective computing Algorithms Biometria Biometry Biometría compound emotions Compounds conjunt de dades d'emocions conjunto de datos de emociones Datasets Dominant and complementary emotion recognition emociones compuestas emocions compostes Emotion recognition Emotions Face recognition Feature extraction fine-grained face emotion dataset Gold Labels Machine learning Object recognition reconeixement d'emocions dominants i complementàries reconocimiento de emociones dominantes y complementarias Task analysis
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Summary:	Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.
ISSN:	2169-3536 2169-3536
DOI:	10.1109/ACCESS.2018.2831927