Paper Info
Title
Model-based signature verification with rotation invariant features
Abstract
Non-linear rotation of signature patterns is one of the major difficulties to solve in off-line signature verification. This paper presents two models utilizing rotation invariant structure features to tackle the problem. In principle, the elaborately extracted ring-peripheral features are able to describe internal and external structure changes of signatures periodically. In order to evaluate match score quantitatively, discrete fast fourier transform is employed to eliminate phase shift and verification is conducted based on a distance model. In addition, the ring-hidden Markov model (HMM) is constructed to directly evaluate similar between test signature and training samples. With respect to the side effect of outlier training samples for stable statistical model and threshold estimation, we propose a selection strategy to improve the performance of system. Experimental results demonstrated that the proposed methods were effective to improve verification accuracy.
Year
DOI
Venue
2009
10.1016/j.patcog.2008.10.006
Pattern Recognition
Keywords
Field
DocType
non-linear rotation,off-line signature verification,external structure change,distance model,rotation invariant feature,outlier training sample,test signature,ring-hidden markov model,signature pattern,stable statistical model,model-based signature verification,verification accuracy,side effect,statistical model,fast fourier transform,phase shift,structural change,hmm,hidden markov model
Rotational invariance,Pattern recognition,Markov model,Outlier,Handwriting recognition,Fast Fourier transform,Invariant (mathematics),Artificial intelligence,Statistical model,Hidden Markov model,Mathematics
Journal
Volume
Issue
ISSN
42
7
Pattern Recognition
Citations 
PageRank 
References 
25
1.01
19
Authors
4
Name
Order
Citations
PageRank
Jing Wen11879.31
Bin Fang278453.47
Y. Y. Tang3416165.12
Taiping Zhang442420.45