Head Pose Estimation using Extracted Elliptical Region of 3d Face Image and Features Based on 3d Geometry

Head pose estimation is the main step of the face recognition process. Finding a method which can be fully automated and capable of responding to different poses in all aspects, has created a challenge for this topic. Compared to face detection and recognition, which have been the primary focus of face-related vision research, the head pose estimation is an inter processing step during the implementation of programs in machine vision systems. In this paper, a new algorithm is presented which first the face region is extracted from 3D head image and then the feature vectors that strongly influence the human perception of head pose and these are extremely salient cues regarding the orientation of the head are extracted. Finally, the extracted vectors are trained as input to the classifiers for comparing and differentiating features. In this paper, the Support vector Machines (SVM), the Radial Basis Function neural network classifier (RBF) and the K-nearest neighbor (KNN) methods were used to train feature vectors and data classification.The algorithm was tested on Frav3D and Gavab databases. Finally the experimental results of 98.48 percent of correct estimates which obtained from KNN method, indicate considerable enhancement compared to previous methods.