Strategies for Improving Face Recognition from Video.

In this chapter, we look at face recognition from video. We exploit the fact that in a given sequence there are multiple images available per subject. We devise strategies to select frames from the video sequences based on quality and difference from each other, so as to improve recognition performance. We compare the four approaches using a video dataset collected at the University of Notre Dame. We compare two pieces of commercially available software to see how they perform when using different data sources. Finally, we experiment with clips that consist of the subject walking in the clip and compare it to using clips where the subject is sitting and talking. We show that multiframe approaches perform better than single frame approaches and that quality of the frames combined with a difference between frames improves performance better than either property alone. [ABSTRACT FROM AUTHOR]