Affiliation:
1. Department of Information Science and Engineering, B. M. S. College of Engineering, Bangalore, India
Abstract
A system is proposed that considers minimal features using subpattern analysis which leads to less response time in a real time scenario. Using training samples, with a high degree of certainty, the minimum variance quadtree components [MVQC] of a signature for a person are listed to be applied on a testing sample. Initially the experiment was conducted on wavelet decomposed information for a signature. The non-MVQCs and core components were analyzed. To characterize the local details Gaussian-Hermite moment was applied. Later Hu moments were applied on the selected subsections. The summation values of the subsections are provided as feature to radial basis function [RBF] and feed forward neural network classifiers. Results indicate that the RBF classifier yielded 7% false rejection rate and feed forward neural network classification technique produced 9% false rejection rate. Promising results were achieved, by experimenting on the list of most prominent minimum variance components which are core components using RBF.
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Cited by
1 articles.
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1. A STRUCTURAL DISTANCE-BASED CROSSOVER FOR NEURAL NETWORK CLASSIFIERS;International Journal of Pattern Recognition and Artificial Intelligence;2012-09