Periodicity attracts special attention in human cognition. Hence it is important to consider that in automatic analysis of motion events. This paper presents a method for representing periodic events with which events can be compared irrespective of their duration. The effectiveness of such a representation is verified with event classification.

Human face recognition is considered to be one of the toughest problems in the domain of pattern recognition. The variations in face images due to differing expression, pose and illumination are some of the key issues to be addressed in developing a face recognition system. In this paper, a new measure called gray Hausdorff distance (denoted by Hpg) is proposed to compare the gray images of faces directly. An efficient algorithm for computation of the new measure is presented. The computation time is linear in the size of the image. The performance of this measure is evaluated on benchmark face databases. The face recognition system based on the new measure is found to be robust to pose and expression variations, as well as to slight variation in illumination. Comparison studies show that the proposed measure performs better than the existing ones in most cases