Computationally efficient statistical face model in the feature space

In this paper, we present a computationally efficient statistical face modeling approach. The efficiency of our proposed approach is the result of mathematical simplifications in the core formula of a previous face modeling method and the use of the singular value decomposition. In order to reduce the errors in our resulting models, we preprocess the facial images to normalize for pose and illumination and remove little occlusions. Then, the statistical face models for the enrolled subjects are obtained from the normalized face images. The effects of the variations in pose, facial expression, and illumination on the accuracy of the system are studied. Experimental results demonstrate the reduction in the computational complexity of the new approach and its efficacy in modeling the face images.