Feature Vector Extraction in HSV with Bandlet Transform

DOI

10.2991/icicci-15.2015.24How to use a DOI?

Keywords

Keywords-Handwritten Signature Verification (HSV); feature vector extraction; bandlet transform; fractal dimension

Abstract

Abstract—Handwritten Signature Verification (HSV) is a discipline which aims to validate the identity of writers according to the handwriting styles. Off-line HSVs compared with on-line ones are more adaptive in equipment involvement and can be applied in more fields, but more difficult to manipulate due to the loss of dynamic writing information such as writing position, velocity, acceleration and pressure. In this paper, we focus on off-line HSV and present a new feature extraction method based on Bandlet and fractal dimension, which gives full play to the merits of both conventional structure feature and statistical feature. After dimensionality reduction with K—L transform, genuine signatures and forgeries are distinguished with support vector machines (SVM). The experimental result confirms the effectiveness of our method.

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  - Ming Yang
PY  - 2015/09
DA  - 2015/09
TI  - Feature Vector Extraction in HSV with Bandlet Transform
BT  - Proceedings of the 2nd International Conference on Intelligent Computing and Cognitive Informatics
PB  - Atlantis Press
SP  - 108
EP  - 110
SN  - 1951-6851
UR  - https://doi.org/10.2991/icicci-15.2015.24
DO  - 10.2991/icicci-15.2015.24
ID  - Yang2015/09
ER  -