Minimum Variance Method to Obtain the Best Shot in Video and its Effectiveness for Face Recognition

This paper describes a face recognition algorithm using feature points of face parts, which is classified as a feature-based method. As recognition performance depends on the combination of extracted feature points, we utilize all reliable feature points effectively. From moving video input, well-conditioned face images with a frontal direction and without facial expression are extracted. To select such well-conditioned images, an iteratively minimizing variance method is used with variable input face images. This iteration drastically brings convergence to the minimum variance of 1 for a quarter to an eighth of all data, which proves to take the frontal image in 0.27 second from video at most. The proposed system using six statistic values realizes 98.3% as an authentication rate.