Veuillez utiliser cette adresse pour citer ce document : http://hdl.handle.net/123456789/6697
Titre: AUTOMATIC AGE ESTIMATION AND GENDER CLASSIFICATION IN THE WILD
Auteur(s): SE. Bekhouche
A. Ouafi
A. Benlamoudi
A. Taleb-Ahmed
A. Hadid
Mots-clés: Age estimation, Gender classification, Local Binary Pattern, Support Vector Machines
Date de publication: 19-déc-2015
Résumé: Automatic age estimation and gender classification through facial images are attractive topics in computer vision. They can be used in many real-life applications such as face recognition and internet safety for minors. In this paper, we present a novel approach for age estimation and gender classification under uncontrolled conditions following the standard protocols for fair comparaison. Our proposed approach is based on Multi Level Local Binary Pattern (ML-LBP) features which are extracted from normalized face images. Two different Support Vector Machines (SVM) models are used to predict the age group and the gender of a person. The experimental results on benchmark Image of Groups dataset showed the superiority of our approach compared to that of the state-ofthe- art methods.
URI/URL: http://dspace.univ-biskra.dz:8080/jspui/handle/123456789/6697
Collection(s) :Communications Internationales

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