A new architecture is presented for multi-map, self-organizing pattern recognition which allows concurrent massively parallel learning of features using different maps for each feature type. The method used is similar to the multi-map structures known to exist in the vertebrate sensory cortex. The learning used to update memory locations uses a feed-forward mechanism and is self-organizing. The architecture is described by the acronym FAUST (Feed-forward Association Using Symmetrical Triggering). As a demonstration of the effectiveness of FAUST, a character recognition, fingerprint classification, and forms recognition programs have been constructed on a massively parallel compute. The character recognition program can perform 99% accurate character recognition on medium-quality machine printed digits at a speed of 2.4 ms/digit, and 88% recognition on multiple-writer handprint with a 2.3% substitutional error rate. The form recognition program can achieve 94% accuracy on complex forms. The fingerprint classification program classifies 93% of fingerprints correctly with 10% rejection rate. All of the calculations were performed on an Active Memory Technology DAP 510.
Charles L. Wilson,
"FAUST: a vision-based neural network multimap pattern recognition architecture", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140110; https://doi.org/10.1117/12.140110