Intelligent Global Face Recognition

Abstract

Face recognition by humans is a natural process that we perform on daily basis. A quick glance at a face and we are able to recognize the face and, most of the time, name the person. Such a process occurs so quickly that we never think of what exactly we looked at in that face. Some of us may take a longer time while trying to name the person, however, the recognition of the familiar face is usually instantaneous. The complexity of a human face arises from the continuous changes in the facial features that take place over time. Despite these changes, we humans are still able to recognize faces and identify the persons. Of course, our natural recognition ability extends beyond face recognition, where we are equally able to quickly recognize patterns, sounds and smells. Unfortunately, this natural ability does not exist in machines, thus the need for artificially simulating recognition in our attempts to create intelligent autonomous machines. Face recognition by machines can be invaluable and has various important applications in real life, such as, electronic and physical access control, national defense and international security. Simulating our face recognition natural ability in machines is a difficult task, but not impossible. Throughout our life time, many faces are seen and stored naturally in our memories forming a kind of database. Machine recognition of faces requires also a database which is usually built using facial images, where sometimes different face images of a one person are included to account for variations in facial features. Current face recognition methods rely on: detecting local facial features and using them for face recognition or on globally analyzing a face as a whole. The first approach (local face recognition systems) uses facial features within the face such as (eyes, nose and mouth) to associate the face with a person. The second approach (global face recognition systems) uses the whole face for identifying the person. This chapter reviews some known existing face recognition methods and presents one case study of a recently developed intelligent face recognition system that uses global pattern averaging for facial data encoding prior to training a neural network using the averaged patterns. The development of intelligent systems that use neural networks is fascinating and has lately attracted more researchers into exploring the potential applications of such systems. The idea of simulating the human perceptions and modeling our senses using machines is great and may help humankind in medical advancement, space exploration, finding alternative energy resources or providing national and international security and peace. Intelligent systems are being increasingly developed aiming to simulate our perception of