On-line signature verification based on spatio-temporal correlation

Authors:
Hao-Ran Deng;Yun-Hong Wang
Affiliations:
Intelligent Recognition and Image Processing Laboratory, School of Computer Science and Engineering, Beihang University, Beijing, China;Intelligent Recognition and Image Processing Laboratory, School of Computer Science and Engineering, Beihang University, Beijing, China
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

Citing 8
Cited 1

The State of the Art in Online Handwriting Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Signature Verification by Dynamic Time-Warping

ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recent Advancements in Automatic Signature Verification

IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Local and Global Feature Selection for On-line Signature Verification

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
HMM-based on-line signature verification: Feature extraction and signature modeling

Pattern Recognition Letters
Automatic Signature Verification: The State of the Art

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

A survey of on-line signature verification

CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a novel signature verification algorithm based on spatio-temporal correlation and an improved template selection method are proposed. The proposed algorithm computes the spatio-temporal matrices of signatures and considers them as images, then applies image processing methods to obtain features. The spatio-temporal matrices will enlarge the variations of the signatures, so an improved template selection method considering the intra-class variation of enrollment signatures is also adopted. The algorithms are validated on the SVC 2004 database and inspiring results are obtained.