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Max-C and min-D projection autoassociative fuzzy morphological memories(max-C and min-D PAFMMs) are two layer feedforward fuzzy morphological neuralnetworks able to implement an associative memory designed for the storage andretrieval of finite fuzzy sets or vectors on a hypercube. In this paper weaddress the main features of these autoassociative memories, which includeunlimited absolute storage capacity, fast retrieval of stored items, fewspurious memories, and an excellent tolerance to either dilative noise orerosive noise. Particular attention is given to the so-called PAFMM of Zadehwhich, besides performing no floating-point operations, exhibit the largestnoise tolerance among max-C and min-D PAFMMs. Computational experiments revealthat Zadeh's max-C PFAMM, combined with a noise masking strategy, yields a fastand robust classifier with strong potential for face recognition.

Max-C and Min-D Projection Autoassociative Fuzzy Morphological Memories: Theory and an Application for Face Recognition