The complexity in face recognition emerges from the variability of the appearance of human faces. While the identity is preserved, the appearance of a face may change due to factors such as illumination, facial pose or facial expression. Reliable biometric identification relies on an appropriate response to these factors. In this paper we address the estimation of the facial pose as a first step to deal with pose changes. We present a method for pose estimation from two-dimensional images captured under active infrared illumination using a statistical model of facial appearance. An active appearance model is fitted to the target image to find facial features. We formulate the fitting algorithm using a smooth warp function, namely thin plate splines. The presented algorithm requires only a coarse and generic three-dimensional model of the face to estimate the pose from the detected features locations. The desired field of application requires the algorithm to work with many di erent faces, including faces of subjects not seen during the training stage. A special focus is therefore on the evaluation of the generalization performance of the algorithm which is one weakness of the classic active appearance model algorithm.
