Modular Neural Networks with granular fuzzy integration for human recognition

Author

Sánchez, Daniela ; Melin, Patricia

Author_Institution

Tijuana Inst. of Technol., Tijuana, Mexico

fYear

2012

fDate

6-8 Aug. 2012

Firstpage

1

Lastpage

6

Abstract

In this paper a new model of a Modular Neural Network (MNN) with fuzzy integration using a granular approach is proposed. The main goal of the proposed approach is to obtain an optimal number of sub modules and optimal percentage of data for training in the MNN. The model was applied to pattern recognition based on the ear and voice biometrics. The proposed method is able to divide the data automatically into sub modules, to work with a percentage of images and select which are the optimal images to be used for training. Also a Hierarchical Genetic Algorithm (HGA) for MNN optimization is proposed. Finally, fuzzy logic as a method for MNN response integration of these biometrics measures is used.

Keywords

biometrics (access control); fuzzy logic; genetic algorithms; image processing; neural nets; pattern recognition; HGA; MNN; ear biometrics; fuzzy logic; granular fuzzy integration; hierarchical genetic algorithm; human recognition; modular neural networks; optimal images; pattern recognition; voice biometrics; Biometrics; Ear; Fuzzy logic; Genetic algorithms; Multi-layer neural network; Training; Granular computing; Hierarchical Genetic Algorithms; Modular Neural Networks; Type-2 Fuzzy Logic;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American

Conference_Location

Berkeley, CA

ISSN

pending

Print_ISBN

978-1-4673-2336-9

Electronic_ISBN

pending

Type

conf

DOI

10.1109/NAFIPS.2012.6290985

Filename

6290985