Offline Signature Identification by Fusion of Multiple Classifiers using Statistical Learning Theory

2010/03/30 by Dakshina Ranjan Kisku, Kisku, Dakshina Ranjan, Phalguni Gupta +3
Computer Science · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #D.2.2 #FOS: Computer and information sciences #Face and Expression Recognition #Handwritten Text Recognition Techniques #I.2.10 #Machine Learning (cs.LG) #cs.CV #cs.LG

paper · pdf · doi:10.48550/arxiv.1003.5865

11 pages, 3 figures, IJSIA 2010

arxiv created 2010/03/30 · openalex publication_date 2010/03/30 · arxiv updated 2010/03/31 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

This paper uses Support Vector Machines (SVM) to fuse multiple classifiers for an offline signature system. From the signature images, global and local features are extracted and the signatures are verified with the help of Gaussian empirical rule, Euclidean and Mahalanobis distance based classifiers. SVM is used to fuse matching scores of these matchers. Finally, recognition of query signatures is done by comparing it with all signatures of the database. The proposed system is tested on a signature database contains 5400 offline signatures of 600 individuals and the results are found to be promising.

Citations

• An Introduction to Biometric Recognition

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