AUTOMATIC FACIAL FEATURE DETECTION FOR FACIAL EXPRESSION RECOGNITION

Taner Danisman, Marius Bilasco, Nacim Ihaddadene, Chabane Djeraba

2010

Abstract

This paper presents a real-time automatic facial feature point detection method for facial expression recognition. The system is capable of detecting seven facial feature points (eyebrows, pupils, nose, and corners of mouth) in grayscale images extracted from a given video. Extracted feature points then used for facial expression recognition. Neutral, happiness and surprise emotions have been studied on the Bosphorus dataset and tested on FG-NET video dataset using OpenCV. We compared our results with previous studies on this dataset. Our experiments showed that proposed method has the advantage of locating facial feature points automatically and accurately in real-time.

References

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Paper Citation


in Harvard Style

Danisman T., Bilasco M., Ihaddadene N. and Djeraba C. (2010). AUTOMATIC FACIAL FEATURE DETECTION FOR FACIAL EXPRESSION RECOGNITION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 407-412. DOI: 10.5220/0002838404070412


in Bibtex Style

@conference{visapp10,
author={Taner Danisman and Marius Bilasco and Nacim Ihaddadene and Chabane Djeraba},
title={AUTOMATIC FACIAL FEATURE DETECTION FOR FACIAL EXPRESSION RECOGNITION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={407-412},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002838404070412},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - AUTOMATIC FACIAL FEATURE DETECTION FOR FACIAL EXPRESSION RECOGNITION
SN - 978-989-674-029-0
AU - Danisman T.
AU - Bilasco M.
AU - Ihaddadene N.
AU - Djeraba C.
PY - 2010
SP - 407
EP - 412
DO - 10.5220/0002838404070412