The goal of this paper is to present a critical survey of the
literature on human and machine recognition of faces. Machine recognition
of faces has several applications ranging from static matching of
controlled photographs as in mugshot matching and credit card
verification to surveillance video images. These applications have
different constraints in terms of the complexity of their processing
requirements and thus present a wide range of technical challenges. Over
the last twenty years researchers in psychophysics, neural sciences and
engineering, image processing, analy sis and computer vision have
investigated a number of issues related to face recognition by humans and
machines. The ongoing research activities have been given renewed emphasis
over the last five years. The existing techniques and systems have been
tested on different sets of images of varying complexities. But very
little synergism exists between studies in psychophysics and Engineering
literature. Most importantly, there exist no evaluation or benchmarking
studies using large databases with the image quality that arises in law
enforcement/commercial applications. 

	In this paper, we first present different applications of face
recognition in the law enforcement and commercial sectors. Special
constraints that are present in these applications are pointed out. This
is followed by a brief overview of the literature o n face recognition in
the psychophysics community. We then present a detailed overview of more
than twenty years of research done in the engineering community.
Techniques for segmentation/location of the face, feature extraction and
recognition are review ed Global transform and feature based methods using
statistical, structural and neural classifiers are summarized. A brief
summary of recognition using face profiles and range image data is also
given. 

	Real-time recognition from video images acquired in a cluttered
scene such as an airport is probably the most challenging face recognition
problem. As not much has been reported on this problem, we discuss several
existing technologies in the image under standing literature that could
potentially impact this problem. 

	Given the numerous theories and techniques that are applicable to face 
recognition, it is clear that evaluation and benchmarking of these 
algorithms is crucial. We discuss relevant issues such as data 
collection, performance metrics and evaluation of systems and 
techniques. Finally, a summary and conclusions are given.
The postscript version of this TR is available from the Center 
for Automation Research via anonymous ftp at ftp.cfar.umd.edu; or via
the WWW at http://www.cfar.umd.edu/CfAR/TRs.
(Also cross-referenced as CAR-TR-731)