Robust head pose estimation using contourlet transform

Head pose estimation is an important pre-processing step in many pattern recognition and computer vision systems such as face recognition. Since the performance of face recognition systems is greatly affected by the pose of the face, how to estimate the accurate pose of the face is still a challenging problem. In this paper, we present a novel method for head pose estimation. To enhance the efficiency of the estimation, we first use contourlet transform for feature extraction which is a multi-resolution, multi-directional transform. In order to reduce the feature space dimension and obtain appropriate features, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to remove inefficient features. Then, K-nearest neighbor (KNN) and minimum distance classifiers are applied separately to classify the pose of head. We use the public available FERET database to evaluate the performance of the proposed method. Simulation results indicate the efficiency of the proposed method in comparison with previous methods.