Veuillez utiliser cette adresse pour citer ce document : http://hdl.handle.net/123456789/6645
Titre: Facial age estimation and gender classification using Multi Level Local Phase Quantization
Auteur(s): Salah Eddine Bekhouche
Abdelkrim Ouafi
Azeddine Benlamoudi
Abdelmalik Taleb-Ahmed
Abdenour Hadid
Mots-clés: Age estimation, Gender classification, Local Phase Quantization, Support Vector Machines
Date de publication: 17-déc-2015
Résumé: Facial demographic classification is an attractive topic in computer vision.Attributes such as age and gender can be used in many real life application 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 Phase Quantization (ML-LPQ) 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 the benchmark Image of Groups dataset showed the superiority of our approach compared to the state-of-the-art.
URI/URL: http://dspace.univ-biskra.dz:8080/jspui/handle/123456789/6645
Collection(s) :Communications Internationales

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