Face shape classification from 3D human data by using SVM

Face shape is also important information for glasses design companies. In this paper, we proposed a non-contact method to classify the face shape by using Support Vector Machine (SVM) technique. This algorithm consists of three steps: head segmentation, face plane identification, and face shape classification. First, as whole 3D body data is captured and used as input of system, Eigenvector is used to define frontal side. Chin-Neck junction, Ellipsoid Fitting Technique and Mahalanobis distance are combined as a head segmentation algorithm to segment the 3D head. Second, face shape can be observed when projected on a plane. Major axes of ellipsoid are used to define a plane along the head called the face plane. Face shape on the face plane is classified into four classes in third step. To test the performance of the proposed method, ninety subjects are used. SVM is used to classify the face shape into four groups. The four type of the face shape are ellipse shape, long shape, round shape, and square shape. The accuracy rate is 73.68%. The result shows the feasibility of the proposed method. An advantage of this method is that this method is first fully automatic and non-contact face shape classification for whole 3D human body data.