Pose normalization for robust face recognition based on statistical affine transformation
A framework for pose-invariant face recognition using the pose alignment method is described in this paper. The main idea is to normalize the face view in depth to frontal view as the input of face recognition framework. Concretely, an inputted face image is first normalized using the irises information, and then the pose subspace algorithm is employed to perform the pose estimation. To model the pose-invariance, the face region is divided into three rectangles with different mapping parameters in this pose alignment algorithm. So the affine transformation parameters associated with the different poses can be used to align the input pose image to frontal view. To evaluate this algorithm objectively, the views after the pose alignment are incorporated into the frontal face recognition system. Experimental results show that it has the better performance and it increases the recognition rate statistically by 17.75% under the pose that rotated within 30 degree.