Temporal normalization of videos using visual speech

Pose and illumination variation has been considered the major cause of poor recognition results in automatic face recognition as compared to other biometrics. With the advent of video based face recognition a decade ago we were presented with some new opportunities, algorithms were developed to take advantage of the abundance of data and behavioral aspect of recognition. But this modality introduced some new challenges also, one of them was the variation introduced by speech. In this paper we present a novel method for handling this variation by using temporal normalization based on lip motion. Evaluation was carried out by comparing face recognition results from original non-normalized videos and normalized videos.