Efficient off-line verification and identification of signatures by multiclass support vector machines

Authors:
Emre Özgündüz;Tülin Şentürk;M. Elif Karslıgil
Affiliations:
Computer Engineering Department, Yıldız Technical University, Yıldız, Istanbul, Turkey;Computer Engineering Department, Yıldız Technical University, Yıldız, Istanbul, Turkey;Computer Engineering Department, Yıldız Technical University, Yıldız, Istanbul, Turkey
Venue:
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Year:
2005

Citing 3
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries

ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a novel and efficient approach for off-line signature verification and identification using Support Vector Machine. The global, directional and grid features of the signatures were used. In verification, one-against-all strategy is used. The true acceptance rate is 98% and true rejection rate is 81%. As the identification of signatures represent a multi-class problem, Support Vector Machine's one-against-all and one-against-one strategies were applied and their performance were compared. Our experiments indicate that one-against-one with 97% true recognition rate performs better than one-against-all by 3%.