Fusion of features and classifiers for off-line handwritten signature verification

Author

Hu, Juan ; Chen, Youbin

Author_Institution

Grad. Sch. at Shenzhen, Tsinghua Univ., Shenzhen, China

fYear

2011

fDate

28-28 Nov. 2011

Firstpage

174

Lastpage

178

Abstract

A method for writer-independent off-line handwritten signature verification based on grey level feature extraction and Real Adaboost algorithm is proposed. Firstly, both global and local features are used simultaneously. The texture information such as co-occurrence matrix and local binary pattern are analyzed and used as features. Secondly, Support Vector Machines (SVMs) and the squared Mahalanobis distance classifier are introduced. Finally, Real Adaboost algorithm is applied. Experiments on the public signature database GPDS Corpus show that our proposed method has achieved the FRR 5.64% and the FAR 5.37% which are the best so far compared with other published results.

Keywords

feature extraction; grey systems; handwriting recognition; handwritten character recognition; image classification; image fusion; image texture; support vector machines; GPDS Corpus; Real Adaboost algorithm; SVM; cooccurrence matrix; feature-classifier fusion; grey level feature extraction; local binary pattern; public signature database; squared Mahalanobis distance classifier; support vector machines; texture information; writer-independent off-line handwritten signature verification; Databases; Feature extraction; Forgery; Support vector machines; Training; Vectors; Grey level information; Off-line handwritten signature verification; Real Adaboost; SVM; Texture features;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition (ACPR), 2011 First Asian Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4577-0122-1

Type

conf

DOI

10.1109/ACPR.2011.6166701

Filename

6166701