Fingerprint classification system with feedback mechanism based on genetic algorithm

2002 ◽  
Author(s):  
Yuan Qi ◽  
Jie Tian ◽  
Ru-Wei Dai
Keyword(s):  
Genetic Algorithm ◽  
Classification System ◽  
Feedback Mechanism ◽  
Fingerprint Classification

scholarly journals Research and implementation of integrated fingerprint classification system based on backtracking feedback

2018 ◽  
Vol 246 ◽  
pp. 03030
Author(s):  
Han Jian Ning
Keyword(s):  
Classification System ◽  
Recognition Rate ◽  
Research Direction ◽  
Recognition System ◽  
Feedback Mechanism ◽  
Fingerprint Recognition ◽  
Fingerprint Classification ◽  
Intelligent Recognition ◽  
Important Research Direction ◽  
Experimental Comparisons

Fingerprint classification has always been an important research direction in the field of intelligent recognition. Based on the method of fingerprint classifier integration, the backtracking feedback mechanism is introduced, and a fingerprint classification system with high recognition rate is designed. Through the use of 1000 fingerprint images in the fingerprint library to test, The system show the recognition results due to the current Kalle Karu, anli K.jain design of a variety of fingerprint recognition system. Through a series of experimental comparisons, it is proved that the fingerprint classification recognition system with the feedback mechanism has better ability of fingerprint recognition, and greatly reduces the error rate of system recognition.

Feature Selection for Fatigue Segment Classification System Using Elitist Non-Dominated Sorting in Genetic Algorithm

2012 ◽  
Vol 165 ◽  
pp. 232-236 ◽  
Author(s):  
Mohd Haniff Osman ◽  
Z.M. Nopiah ◽  
S. Abdullah
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Classification System ◽  
Learning Algorithm ◽  
Selection Procedure ◽  
Classification Error ◽  
Algorithm Selection ◽  
Nsga Ii ◽  
Nearest Neighbours ◽  
Good Set

Having relevant features for representing dataset would motivate such algorithms to provide a highly accurate classification system in less-consuming time. Unfortunately, one good set of features is sometimes not fit to all learning algorithms. To confirm that learning algorithm selection does not weights system accuracy user has to validate that the given dataset is a feature-oriented dataset. Thus, in this study we propose a simple verification procedure based on multi objective approach by means of elitist Non-dominated Sorting in Genetic Algorithm (NSGA-II). The way NSGA-II performs in this work is quite similar to the feature selection procedure except on interpretation of the results i.e. set of optimal solutions. Two conflicting minimization elements namely classification error and number of used features are taken as objective functions. A case study of fatigue segment classification was chosen for the purpose of this study where simulations were repeated using four single classifiers such as Naive-Bayes, k nearest neighbours, decision tree and radial basis function. The proposed procedure demonstrates that only two features are needed for classifying a fatigue segment task without having to place concern on learning algorithm

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