A Fast and Robust Max-C Projection Fuzzy Autoassociative Memory with Application for Face Recognition
| Autor: | Alex Santana dos Santos, Marcos Eduardo Valle |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Recall business.industry Computer science Fuzzy set Pattern recognition 02 engineering and technology Content-addressable memory Fuzzy logic Facial recognition system Autoassociative memory 020901 industrial engineering & automation Atmospheric measurements Robustness (computer science) TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITY 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business MathematicsofComputing_DISCRETEMATHEMATICS |
| Zdroj: | BRACIS |
| DOI: | 10.1109/bracis.2017.57 |
| Popis: | Max-C projection autoassociative fuzzy memories (max-C PAFMs) are memory models designed for the storage and recall of fuzzy sets. In few words, a max-C PAFM projects the input fuzzy set into the family of all max-C combinations of the stored items. In this paper, we focus on a particular max-C PAFM called Zadeh max-C PAFM. The Zadeh max-C PAFM is the most robust max-C PAFM with respect to dilative noise. Furthermore, by masking the noise contained in a corrupted input, it exhibits excellent tolerance to any kind of noise. Besides introducing the Zadeh max-C PAFM, in this paper we point out a potential application of the Zadeh max-C PAFM for face recognition. |
| Databáze: | OpenAIRE |
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