Abstract
A neural network based facial recognition program (FADER—FAce DEtection and Recognition) was developed and tested. The hardware and software components were all standard commercial design, allowing the system to be built for minimal cost. Using a set of 1000 face and 1000 ‘no-face’ images, we achieved 94.7% detection rate, and a 0.6% false positive rate. Three different neural network models were applied to face recognition, using single images of each subject to train the system. A novel adaptation of the Hebbian connection strength adjustment model gave the best results, with 74.1% accuracy achieved. Each of the system's components, including an intermediate substructure detection network, was subject to evolutionary computation in order to optimise the system performance.
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