Face recognition is a challenging field of research not only because of the complexity of this subject, but also because of its numerous practical applications. Much progress has been made towards recognising faces under controlled conditions, especially under normalised pose and lighting conditions and with neutral expression. However, the recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. This is due to the fact that most of face recognition methods assume that the pose of the face is known.

In this paper, we propose the use of a Gabor Filter Bank to extract an augmented Gabor-face vector to solve the pose estimation problem, extract some statistical features such as means and variances. And then the classification is performed using the nearest neighbour algorithm with the Euclidean distance. Finally, experimental results are reported to show the robustness of the extracted feature vectors for the recognition problem.