Invariant pattern recognition using analog recurrent associative memories
A novel invariant pattern recognition approach is proposed based on a special gradient-type recurrent analog associative memory. The system exhibits stable equilibrium points in predefined positions specified by feature vectors extracted from the training set, while invariance to geometrical transformations is inferred by using the tangent distance. Experimental results for handwritten character recognition and face recognition tasks indicate that the proposed approach may yield superior performances over classical solutions based on the Euclidean distance metric. Possible extensions towards modular and sequential pattern recognition are finally outlined.
invariance associative memory tangent distance stable equilibria