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.
引用
收藏
页码：2 / 12
页数：11
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