The use of 3-Dimensional information in face recognition requires pose estimation. We present the use of 3-Dimensional composite correlation filter to obtain pose estimation without the need for feature identification. Composite correlation filter research has been vigorously pursued in the last three decades due to their applications in many areas, but mainly in distortion-invariant pattern recognition. While most of this research is in two-dimensional space, we have extended our study of composite filters to three-dimensions, specifically emphasizing Linear Phase Coefficient Composite Filter (LPCCF). Unlike previous approaches to composite filter design, this method considers the filter design and the training set selection simultaneously. In this research, we demonstrate the potential of implementing LPCCF in head pose estimation. We introduce the utilization of LPCCF in the application of head pose recovery through full correlation using a set of 3-D voxel maps instead of the typical 2-D pixel images/silhouettes. Unlike some existing approaches to pose estimation, we are able to acquire 3-D head pose without locating salient features of a subject. In theory, the correlation phase response contains information about the angle of head rotation of the subject. Pose estimation experiments are conducted for two degrees of freedom in rotation, that is, yaw and pitch angles. The results obtained are very much inline with our theoretical hypothesis on head orientation estimation.