Feature Selection for Emotion Recognition based on Random Forest

Sonia Gharsalli, Bruno Emile, Hélène Laurent, Xavier Desquesnes

Abstract

Automatic facial emotion recognition is a challenging problem. Emotion recognition system robustness is particularly difficult to achieve as the similarity of some emotional expressions induces confusion between them. Facial representation needs feature extraction and feature selection. This paper presents a selection method incorporated into an emotion recognition system. Appearance features are firstly extracted by a Gabor filter bank and the huge feature size is reduced by a pretreatment step. Then, an iterative selection method based on Random Forest (RF) feature importance measure is applied. Emotions are finally classified by SVM. The proposed approach is evaluated on the Cohn-Kanade database with seven expressions (anger, happiness, fear, disgust, sadness, surprise and the neutral expression). Emotion recognition rate achieves 95.2% after feature selection and an improvement of 22% for sadness recognition is noticed. PCA is also used to select features and compared to RF base feature selection method. As well, a comparison with emotion recognition methods from literature which use a feature selection step is done.

References

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Paper Citation


in Harvard Style

Gharsalli S., Emile B., Laurent H. and Desquesnes X. (2016). Feature Selection for Emotion Recognition based on Random Forest . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 610-617. DOI: 10.5220/0005725206100617


in Bibtex Style

@conference{visapp16,
author={Sonia Gharsalli and Bruno Emile and Hélène Laurent and Xavier Desquesnes},
title={Feature Selection for Emotion Recognition based on Random Forest},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={610-617},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005725206100617},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Feature Selection for Emotion Recognition based on Random Forest
SN - 978-989-758-175-5
AU - Gharsalli S.
AU - Emile B.
AU - Laurent H.
AU - Desquesnes X.
PY - 2016
SP - 610
EP - 617
DO - 10.5220/0005725206100617