Abstract
A human face is a complex object with features that can vary over time. However, we humans have a natural ability to recognize faces and identify persons in a glance. Of course, our natural recognition ability extends beyond face recognition, where we are equally able to quickly recognize patterns, sounds or smells. Unfortunately, this natural ability does not exist in machines, thus the need to simulate recognition artificially 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. While the world is in war against terrorism, the list of wanted persons is getting larger, however, in most cases there is a database containing their face images with various different features such as: with and without eyeglasses or bearded and clean shaven...etc. These different face images of persons (wanted or not) can be used as database in the development of face recognition systems.
Current face recognition methods rely on either: detecting local facial features and using them for face recognition or on globally analyzing a face as a whole. This chapter reviews known existing face recognition methods and presents two case studies of recently developed intelligent face recognition systems that use global and local pattern averaging for facial data encoding prior to training a neural network using the averaged patterns.
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Khashman, A. (2008). Intelligent Face Recognition. In: Chen, H., Yang, C.C. (eds) Intelligence and Security Informatics. Studies in Computational Intelligence, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69209-6_20
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DOI: https://doi.org/10.1007/978-3-540-69209-6_20
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