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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. [ABSTRACT FROM AUTHOR]

Neural networks have been used in the development of intelligent recognition systems that simulate our ability recognize patterns. However, rotated objects may cause incorrect identification by recognition systems. Our quick glance provides an overall approximation of a pattern regardless of noise or rotations. This paper proposes that the overall approximation of a pattern can be achieved via pattern averaging prior to training a neural network to recognize that pattern in various rotations. Pattern averaging provides the neural network with "fuzzy" rather than "crisp" representations of the rotated objects, thus, minimizing computational costs and providing the neural network with meaningful learning of various rotations of an object. The proposed method will be used to recognize rotated coins and is implemented to solve an existing problem where slot machines in Europe accept the new Turkish 1 Lira coin as a 2 Euro coin. [ABSTRACT FROM AUTHOR]

Real-life applications of intelligent systems that use neural networks require a high degree of success, usability and reliability. Power systems applications can benefit from such intelligent systems; particularly for voltage stabilization. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. This paper presents an intelligent system which detects voltage instability and classifies voltage output of an assumed power distribution system (PDS) as: stable, unstable or overload. The novelty of our work is the use of voltage output images as the input patterns to the neural network for training and generalizing purposes, thus providing a faster instability detection system that simulates a trained operator controlling and monitoring the 3-phase voltage output of the assumed PDS. Experimental results suggest that our method performs well and provides a fast and efficient system for voltage instability detection. [ABSTRACT FROM AUTHOR]

The human ability to recognize objects has not so far been matched by intelligent machines. This is more evident when it comes to recognizing faces, where a quick human "glance" is sufficient to recognize a "familiar" face. Face recognition has recently attracted more research aimed at developing reliable recognition by machines. Current face recognition methods rely on detecting certain features within a face and using these features for face recognition. This paper introduces a novel approach to face recognition by simulating our ability to recognize "familiar" faces after a quick "glance" using pattern averaging and neural networks. A real-life application will be presented throughout recognizing the faces of 30 persons. Time costs and the neural network parameters will be described, in addition to future work aimed at further improving the developed system. [ABSTRACT FROM AUTHOR]

Over the past four years there has been a marginal increase in research on developing advanced information technologies that can be efficiently used for national and international security in our war against terrorism. The list of wanted persons who are still free is getting larger, however, in most cases there is a database containing their face images and this can be used in the development of face recognition systems. A human face is an extremely complex object with features that can vary over time, sometimes very rapidly. This paper presents a fast intelligent face recognition system that uses essential face features averaging and a neural network to identify multi-expression faces. A real life application using this method is implemented on 180 images of 30 persons. Experimental results suggest that this simple but efficient system performs well, thus providing a fast intelligent system for recognizing faces with different expressions. [ABSTRACT FROM AUTHOR]

When developing intelligent recognition systems, our perception of patterns can be simulated using neural networks. An intelligent coin identification system that uses coin patterns for classification helps prevent confusion between different coins of similar physical dimensions. Currently, coin identification by machines relies on the assessment of the coin's physical parameters. In this paper, a rotation-invariant intelligent coin identification system (ICIS) is presented. ICIS uses a neural network and pattern averaging to recognize rotated coins at various degrees. Slot machines in Europe accept the new Turkish 1-Lira coin as a 2-Euro coin due to physical similarities. A 2-Euro coin is roughly worth 4 times the new Turkish 1-Lira. ICIS was implemented to identify the 2 EURO and 1 TL coins and the results were found to be encouraging. [ABSTRACT FROM AUTHOR]

A human face is a complex object with features that can vary over time. Face recognition systems have been investigated while developing biometrics technologies. This paper presents a face recognition system that uses eyes, nose and mouth approximations for training a neural network to recognize faces in different expressions such as natural, smiley, sad and surprised. The developed system is implemented using our face database and the ORL face database. A comparison will be drawn between our method and two other face recognition methods; namely PCA and LDA. Experimental results suggest that our method performs well and provides a fast, efficient system for recognizing faces with different expressions. [ABSTRACT FROM AUTHOR]

Real-life applications of neural networks require a high degree of success, usability and reliability. Image processing has an importance for both data preparation and human vision to increase the success and reliability of pattern recognition applications. The combination of both image processing and neural networks can provide sufficient and robust solutions to problems where intelligent recognition is required. This paper presents an implementation of neural networks for the recognition of various banknotes. One combined neural network will be trained to recognize all the banknotes of the Turkish Lira and the Cyprus Pound; as they are the main currencies used in Cyprus. The flexibility, usability and reliability of this Intelligent Banknote Identification System (IBIS) will be shown through the results and a comparison will be drawn between using separate neural networks or a combined neural network for each currency. Keywords: Neural Networks, Pattern Recognition, Image Processing, Multi-Banknote Identification. [ABSTRACT FROM AUTHOR]