Fusing Shape and Texture Features for Pose-Robust Face Recognition

Unconstrained environments with variable ambient illumination and changes of head pose are still challenging for many face recognition systems. To recognize a person independent of pose, we separate shape from texture information using an active appearance model. We do not directly use the texture information from the active appearance model for recognition. Instead we extract local texture features from a shape and pose free representation of facial images. We use a smooth warp function to transform the images. We compensate also the shape information for head pose changes and fuse the results of separate classifiers for shape features and local texture features. We analyze the influence of the individual contributions of shape and texture information on the recognition performance. We show that fusing shape and texture information can boost the recognition performance in an access control scenario.