Facial Expression Recognition Using New Feature Extraction Algorithm
Huang, Hung-Fu (National Cheng Kung University (Taiwan). Department of Electrical Engineering)
Tai, Shen-Chuan (National Cheng Kung University (Taiwan). Department of Electrical Engineering)

Date:	2012
Abstract:	This paper proposes a method for facial expression recognition. Facial feature vectors are generated from keypoint descriptors using Speeded-Up Robust Features. Each facial feature vector is then normalized and next the probability density function descriptor is generated. The distance between two probability density function descriptors is calculated using Kullback Leibler divergence. Mathematical equation is employed to select certain practicable probability density function descriptors for each grid, which are used as the initial classification. Subsequently, the corresponding weight of the class for each grid is determined using a weighted majority voting classifier. The class with the largest weight is output as the recognition result. The proposed method shows excellent performance when applied to the Japanese Female Facial Expression database.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Speeded-Up Robust Features ; Probability density function ; Kullback Leibler ; Divergence ; Weighted majority voting
Published in:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 11, Núm. 1 (2012) , p. 41-54, ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v11-n1-huang-tai
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/280896
DOI: 10.5565/rev/elcvia.451

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