Manifold Learning of Overcomplete Feature Spaces in a Multimodal Biometric Recognition System of Iris and Palmprint

2017 ◽  
Author(s):  
Habibeh Naderi ◽  
Behrouz Haji Soleimani ◽  
Stan Matwin
Keyword(s):  
Manifold Learning ◽  
Recognition System ◽  
Biometric Recognition ◽  
Feature Spaces ◽  
Multimodal Biometric Recognition

GENETIC ALGORITHM BASED FEATURE SELECTION LEVEL FUSION USING FINGERPRINT AND IRIS BIOMETRICS

2008 ◽  
Vol 22 (03) ◽  
pp. 585-600 ◽  
Author(s):  
A. ALPASLAN ALTUN ◽  
H. ERDINC KOCER ◽  
NOVRUZ ALLAHVERDI
Keyword(s):  
Feature Selection ◽  
Degrees Of Freedom ◽  
Recognition System ◽  
Test Results ◽  
Biometric Recognition ◽  
Training Time ◽  
Iris Biometrics ◽  
Multimodal Biometric Recognition ◽  
Selection Of ◽  
Combined Data

An accuracy level of unimodal biometric recognition system is not very high because of noisy data, limited degrees of freedom, spoof attacks etc. problems. A multimodal biometric system which uses two or more biometric traits of an individual can overcome such problems. We propose a multimodal biometric recognition system that fuses the fingerprint and iris features at the feature extraction level. A feed-forward artificial neural networks (ANNs) model is used for recognition of a person. There is a need to make the training time shorter, so the feature selection level should be performed. A genetic algorithms (GAs) approach is used for feature selection of a combined data. As an experiment, the database of 60 users, 10 fingerprint images and 10 iris images taken from each person, is used. The test results are presented in the last stage of this research.

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