Off-line Signature Verification Using Linear Regression Classifier

DOI

10.2991/icitmi-15.2015.192How to use a DOI?

Keywords

Linear Regression Classifier, Local Binary Patterns, Tuned Linear Regression Classifier.

Abstract

In this paper we propose a novel classification method based on Linear Regression Classification (LRC) for offline signature verification. The class-specific models can be simply established by using the registered samples (examples), and a test signature can be linearly represented by these registered samples. Then the tuned-LRC is constructed to capture the nonlinear information when the fundamental linear assumption is invalid in LRC. In contrast to the conventional classifiers used in signature verification, our proposed methods are very simple and no training stage is needed, and the dictionary can be easily expanded by additional samples. The experiments conducted on GPDS960Graysignature database demonstrate the effectiveness of the proposed methods.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo Xu
AU  - Daozhi Lin
AU  - Hongyang Chao
AU  - Weifeng Li
AU  - Qingmin Liao
PY  - 2015/10
DA  - 2015/10
TI  - Off-line Signature Verification Using Linear Regression Classifier
BT  - Proceedings of the 4th International Conference on Information Technology and Management Innovation
PB  - Atlantis Press
SP  - 1144
EP  - 1148
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitmi-15.2015.192
DO  - 10.2991/icitmi-15.2015.192
ID  - Xu2015/10
ER  -