What Can Your Computer Recognize: Chemical and Facial Pattern Recognition Through the Use of the Eigen Analysis Method

Seeing patterns in the world is part of the human condition. If the numbers 2, 4, 6, 8,... are put before someone they will readily recognize the pattern of counting by two and be able to continue the sequence with the number 10, 12, . . . . Similarly, someone who is moderately acquainted with mathematics would recognize the numbers 0, 1, 1, 2, 3, 5, 8,... as the Fibonacci sequence. Yet, patterns are not simply limited to what can be observed within mathematical relationships. Yet, while humans can identify the pattern found within the frieze, a computer could not perform the same recognition with the ease or sophistication inherent to the human mind. Even the seemingly simple act of reading and comprehending the sentences on a page is an example of pattern recognition that can be performed with a sense of effortlessness by a human, but with only moderate success by a computer

Weightless neural networks for face recognition

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The interface with the real-world has proved to be extremely challenging throughout the past 70 years in which computer technology has been developing. The problem initially is assumed to be somewhat trivial, as humans are exceptionally skilled at interpreting real-world data, for example pictures and sounds. Traditional analytical methods have so far not provided the complete answer to what will be termed pattern recognition. Biological inspiration has motivated pattern recognition researchers since the early days of the subject, and the idea of a neural network which has self-evolving properties has always been seen to be a potential solution to this endeavour. Unlike the development of computer technology in which successive generations of improved devices have been developed, the neural network approach has been less successful, with major setbacks occurring in its development. However, the fact that natural processing in animals and humans is a voltage-based process, devoid of software, and self-evolving, provides an on-going motivation for pattern recognition in artificial neural networks. This thesis addresses the application of weightless neural networks using a ranking pre-processor to implement general pattern recognition with specific reference to face processing. The evaluation of the system will be carried out on open source databases in order to obtain a direct comparison of the efficacy of the method, in particular considerable use will be made of the MIT-CBCL face database. The methodology is cost effective in both software and hardware forms, offers real-time video processing, and can be implemented on all computer platforms. The results of this research show significant improvements over published results, and provide a viable commercial methodology for general pattern recognition