Max-C and Min-D Projection Autoassociative Fuzzy Morphological Memories: Theory and an Application for Face Recognition
This work addresses the need for robust associative memories in pattern recognition, particularly for face recognition, but it appears incremental as it builds on existing fuzzy morphological neural networks.
The paper tackled the problem of associative memory for fuzzy sets by introducing max-C and min-D projection autoassociative fuzzy morphological memories, which achieved unlimited storage capacity, fast retrieval, and high tolerance to noise, with Zadeh's version showing the best noise tolerance and potential for face recognition.
Max-C and min-D projection autoassociative fuzzy morphological memories (max-C and min-D PAFMMs) are two layer feedforward fuzzy morphological neural networks able to implement an associative memory designed for the storage and retrieval of finite fuzzy sets or vectors on a hypercube. In this paper we address the main features of these autoassociative memories, which include unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and an excellent tolerance to either dilative noise or erosive noise. Particular attention is given to the so-called PAFMM of Zadeh which, besides performing no floating-point operations, exhibit the largest noise tolerance among max-C and min-D PAFMMs. Computational experiments reveal that Zadeh's max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with strong potential for face recognition.