Gender Classification Based on 3D Face Geometry Features Using SVM

Author

Han, Xia ; Ugail, Hassan ; Palmer, Ian

Author_Institution

Sch. of Comput., Inf. & Media, Univ. of Bradford, Bradford, UK

fYear

2009

Firstpage

114

Lastpage

118

Abstract

In this work we have used non-linear Support Vector Machines (SVMs) for gender classification. The SVMis applied to triangular meshes representing human faces. In this work we rely on handful of 3-dimensional facial features which are extracted from the corresponding geometry meshes. The experimental results show that in our method the error rate is 17.44% on average. It is thought that the approach used to determine gender prior to face recognition would make an automatic face recognition system more efficient.

Keywords

face recognition; feature extraction; gender issues; geometry; image classification; support vector machines; 3D face geometry features; SVM; automatic face recognition system; facial feature extraction; gender classification; human faces; nonlinear support vector machines; triangular meshes; Computational geometry; Data mining; Error analysis; Face recognition; Facial features; Humans; Informatics; Information geometry; Support vector machine classification; Support vector machines; SVM; gender classification; geometry features;

fLanguage

English

Publisher

ieee

Conference_Titel

CyberWorlds, 2009. CW '09. International Conference on

Conference_Location

Bradford

Print_ISBN

978-1-4244-4864-7

Electronic_ISBN

978-0-7695-3791-7

Type

conf

DOI

10.1109/CW.2009.41

Filename

5279671