Human and Machine Recognition of Faces: a Survey

The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shoe matching and credit card vel verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges, Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing, analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different see of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exist no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications.In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a derailed overview of more than 20 years of research done in the engineering community, Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. 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 face recognition from video images acquired in a cluttered scene such as an airport is probably the most challenging problem. We discuss several existing technologies in the image understanding 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, summary and conclusions are given.