Off-line Signature Verification (SV) using the Chi-square statistics

被引：1
|
作者：
Das, M. Taylan [1 ]
Dulger, L. Canan [2 ]
Dulger, H. Ergin [3 ]
机构：
[1] Roketsan Co, TR-06780 Elmadag Ankara, Turkey
[2] Fac Engn, Engn Mech Dept, TR-27310 Gaziantep, Turkey
[3] Gaziantep Univ, Fac Med, Forens Sci, TR-27310 Gaziantep, Turkey
来源：
INTERNATIONAL JOURNAL OF BIOMETRICS | 2011年 / 3卷 / 01期
关键词：
SV; signature verification; off-line signature verification; PSO; particle swarm optimisation; NNs; neural networks; the Chi-square statistics;
D O I：
10.1504/IJBM.2011.037711
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Off-line Signature Verification (SV) is performed using Particle Swarm Optimisation-Neural Network (PSO-NN) algorithm. The technique is based on NN approach trained with PSO algorithm. The presented verification system includes image-processing techniques and other mathematical tools in its structure. To test the performance of the proposed algorithm, three types of forgeries, namely random, unskilled and skilled, are examined. A database with 1350 skilled and genuine signatures taken from 25 volunteers is used for testing the algorithm. The experimental results are presented with comparisons on verification accuracy and statistical figures.
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页码：2 / 12
页数：11
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