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

## TL;DR

This paper introduces max-C and min-D fuzzy morphological memories with high storage capacity and noise tolerance, demonstrating their effectiveness in face recognition tasks through computational experiments.

## Contribution

It presents a new class of fuzzy morphological memories with unlimited capacity and robustness, and applies them to face recognition with promising results.

## Key findings

- High noise tolerance of the proposed memories.
- Fast retrieval and large storage capacity.
- Effective face recognition performance.

## Abstract

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.