Generalized Exponential Bidirectional Fuzzy Associative Memory with Fuzzy Cardinality-Based Similarity Measures Applied to Face Recognition

Authors

DOI:

https://doi.org/10.5540/tema.2018.019.02.221

Keywords:

Associative memory, fuzzy set theory, similarity measure, face recognition.

Abstract

Associative memories are biologically inspired models designed for the storage and recall by association. Such models aim to store a finite set of associations, called the fundamental memory set. The generalized exponential bidirectional fuzzy associative memory (GEB-FAM) is a heteroassociative memory model designed for the storage and recall of fuzzy sets. A similarity measure, that is, a function that indicates how much two fuzzy sets are equal, is at the core of a GEB-FAM model. In this paper, we present a detailed study on the use of cardinality-based similarity measures in the definition of a GEB-FAM. Moreover, we evaluate the performance of the GEB-FAMs defined using such measures in a face recognition problem.

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Published

2018-09-12

How to Cite

Souza, A. C., & Valle, M. E. (2018). Generalized Exponential Bidirectional Fuzzy Associative Memory with Fuzzy Cardinality-Based Similarity Measures Applied to Face Recognition. Trends in Computational and Applied Mathematics, 19(2), 221. https://doi.org/10.5540/tema.2018.019.02.221

Issue

Vol. 19 No. 2 (2018)

Section

Original Article

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