The recognition of humans and their activities from video sequences is currently a very active
area of research because of its applications in video surveillance design of realistic
entertainment systems multimedia communications and medical diagnosis. In this lecture we
discuss the use of face and gait signatures for human identification and recognition of human
activities from video sequences. We survey existing work and describe some of the more
well-known methods in these areas. We also describe our own research and outline future
possibilities. In the area of face recognition we start with the traditional methods for
image-based analysis and then describe some of the more recent developments related to the use
of video sequences 3D models and techniques for representing variations of illumination. We
note that the main challenge facing researchers in this area is the development of recognition
strategies that are robust to changes due to pose illumination disguise and aging. Gait
recognition is a more recent area of research in video understanding although it has been
studied for a long time in psychophysics and kinesiology. The goal for video scientists working
in this area is to automatically extract the parameters for representation of human gait. We
describe some of the techniques that have been developed for this purpose most of which are
appearance based. We also highlight the challenges involved in dealing with changes in
viewpoint and propose methods based on image synthesis visual hull and 3D models. In the
domain of human activity recognition we present an extensive survey of various methods that
have been developed in different disciplines like artificial intelligence image processing
pattern recognition and computer vision. We then outline our method for modeling complex
activities using 2D and 3D deformable shape theory. The wide application of automatic human
identification and activity recognition methods will require the fusion of different modalities
like face and gait dealing with the problems of pose and illumination variations and accurate
computation of 3D models. The last chapter of this lecture deals with these areas of future
research.
