  Estimating pose of the head is an important preprocessing step in many
pattern recognition and computer vision systems such as face recognition. Since
the performance of the face recognition systems is greatly affected by the
poses of the face, how to estimate the accurate pose of the face in human face
image is still a challenging problem. In this paper, we represent a novel
method for head pose estimation. To enhance the efficiency of the estimation we
use contourlet transform for feature extraction. Contourlet transform is
multi-resolution, multi-direction transform. In order to reduce the feature
space dimension and obtain appropriate features we use LDA (Linear Discriminant
Analysis) and PCA (Principal Component Analysis) to remove ineffcient features.
Then, we apply different classifiers such as k-nearest neighborhood (knn) and
minimum distance. We use the public available FERET database to evaluate the
performance of proposed method. Simulation results indicate the superior
robustness of the proposed method.
