RECURSIVE LEARNING FUZZY LOGIC MODEL FOR BUILDING FEATURE EXTRACTOR IN IDENTIFICATION BIOMETRIC SYSTEM

Abstract

The objective of this dissertation is the integration of fuzzy logic to biometric systems for person identification. Fuzzy logic is used exactly to build the feature extractor of the biometric system. For this propose a fuzzy model for the image is chosen by selecting Gaussian membership functions for pixel coordinates and constants functions for gray level of pixels. The fuzzy model will be learned by an iterative procedure to extract the vector of characteristics. This biometric system is tested using Matlab software and a database of 500 persons with 12 images for each person. The results of simulation give good results with the FRR, FAR and EER values.