Robust head pose estimation using contourletSD transform and GLCM

Abstract

Head pose estimation is an important preprocessing step in many computer vision and pattern recognition systems such as face recognition. Compared to face detection and recognition which have been wildly used in computer vision systems, head pose estimation has fewer proposed systems and generic solutions. In this paper we propose a novel approach for robust human head pose estimation using contourletSD transform. At first we apply contourletSD transform on images, then we create feature vector by computing gray-level co-occurrence matrix (GLCM) from each contourlet sub-band. Linear discriminant analysis (LDA) is used for dimensionality reduction of feature vector. Finally, we classify obtained feature vectors using Support Vector Machine (SVM), K-nearest Neighbor (KNN) and hierarchical decision tree (HDT) classifiers, separately. Experimental results on FERET database demonstrate robustness of the proposed method than previous methods in human head pose estimation.