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.

New Mechanisms to Enhance the Performances of Arabic Text Recognition System

2017 ◽  
pp. 879-896 ◽  
Author(s):  
Marwa Amara ◽  
Kamel Zidi
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Multilayer Perceptron ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Computing Time ◽  
Recognition System ◽  
Computational Overhead ◽  
Feature Selection Techniques ◽  
Selection Of

The recognition of a character begins with analyzing its form and extracting the features that will be exploited for the identification. Primitives can be described as a tool to distinguish an object of one class from another object of another class. It is necessary to define the significant primitives during the development of an optical character recognition system. Primitives are defined by experience or by intuition. Several primitives can be extracted while some are irrelevant or redundant. The size of vector primitives can be large if a large number of primitives are extracted including redundant and irrelevant features. As a result, the performance of the recognition system becomes poor, and as the number of features increases, so does the computing time. Feature selection, therefore, is required to ensure the selection of a subset of features that gives accurate recognition and has low computational overhead. We use feature selection techniques to improve the discrimination capacity of the Multilayer Perceptron Neural Networks (MLPNNs).

scholarly journals An Automatic Parkinson ’s Disease Recognition System Based on Multi - Feature Selection of Motion Signals

2017 ◽  
Vol 12 ◽  
pp. 03020
Author(s):  
Tian-Yu Shen ◽  
Ji-Ping Wang ◽  
Jing Chen ◽  
Da-Xi Xiong ◽  
Li-Quan Guo
Keyword(s):  
Feature Selection ◽  
Recognition System ◽  
Selection Of
Sign in / Sign up

Export Citation Format

Share Document