Title :
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
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;
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
