On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Identity authentication using improved online signature verification method
Pattern Recognition Letters
A New Off-line Signature Verification Method based on Graph
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Off-line signature verification using DTW
Pattern Recognition Letters
Offline signature verification using the discrete radon transform and a hidden Markov model
EURASIP Journal on Applied Signal Processing
HMM-based on-line signature verification: Feature extraction and signature modeling
Pattern Recognition Letters
Off-line signature verification and forgery detection using fuzzy modeling
Pattern Recognition
Target dependent score normalization techniques and their application to signature verification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Online signature verification with support vector machines based on LCSS kernel functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Off-line signature verification based on multitask learning
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Multitask multiclass support vector machines: Model and experiments
Pattern Recognition
Dynamic signature recognition based on modified windows technique
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
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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.