Facial Expression Recognition Improvement through an Appearance Features Combination

Taoufik Ben Abdallah, Radhouane Guermazi, Mohamed Hammami

Abstract

This paper suggests an approach to automatic facial expression recognition for images of frontal faces. Two methods of appearance features extraction is combined: Local Binary Pattern (LBP) on the whole face region and Eigenfaces on the eyes-eyebrows and/or on the mouth regions. Support Vector Machines (SVM), K Nearest Neighbors (KNN) and MultiLayer Perceptron (MLP) are applied separately as learning technique to generate classifiers for facial expression recognition. Furthermore, we conduct to the many empirical studies to fix the optimal parameters of the approach. We use three baseline databases to validate our approach in which we record interesting results compared to the related works regardless of using faces under controlled and uncontrolled environment.

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Paper Citation


in Harvard Style

Ben Abdallah T., Guermazi R. and Hammami M. (2017). Facial Expression Recognition Improvement through an Appearance Features Combination . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-758-249-3, pages 111-118. DOI: 10.5220/0006288301110118


in Bibtex Style

@conference{iceis17,
author={Taoufik Ben Abdallah and Radhouane Guermazi and Mohamed Hammami},
title={Facial Expression Recognition Improvement through an Appearance Features Combination},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2017},
pages={111-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006288301110118},
isbn={978-989-758-249-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - Facial Expression Recognition Improvement through an Appearance Features Combination
SN - 978-989-758-249-3
AU - Ben Abdallah T.
AU - Guermazi R.
AU - Hammami M.
PY - 2017
SP - 111
EP - 118
DO - 10.5220/0006288301110118