The nature of statistical learning theory
The nature of statistical learning theory
Reliable On-Line Human Signature Verification Systems
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
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Investigation of Off-Line Japanese Signature Verification Using a Pattern Matching
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Local and Global Feature Selection for On-line Signature Verification
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Identity authentication using improved online signature verification method
Pattern Recognition Letters
A comparative study on the consistency of features in on-line signature verification
Pattern Recognition Letters
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
HMM-based on-line signature verification: Feature extraction and signature modeling
Pattern Recognition Letters
On The Effects of Sampling Rate and Interpolation in HMM-Based Dynamic Signature Verification
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Similarity Computation Based on Feature Extraction for Off-line Chinese Signature Verification
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Online signature verification using Fourier descriptors
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Distance-based classification of handwritten symbols
International Journal on Document Analysis and Recognition - Special Issue DRR09
Enhancements on a signature recognition problem
ICCP '10 Proceedings of the Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing
Signature Verification Competition for Online and Offline Skilled Forgeries (SigComp2011)
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Automatic Signature Verification: The State of the Art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Effectiveness of pen pressure, azimuth, and altitude features for online signature verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In this paper, feature combinations associated with the most commonly used time functions related to the signing process are analyzed, in order to provide some insight on their actual discriminative power for online signature verification. A consistency factor is defined to quantify the discriminative power of these different feature combinations. A fixed-length representation of the time functions associated with the signatures, based on Legendre polynomials series expansions, is proposed. The expansion coefficients in these series are used as features to model the signatures. Two different signature styles, namely, Western and Chinese, from a publicly available Signature Database are considered to evaluate the performance of the verification system. Two state-of-the-art classifiers, namely, Support Vector Machines and Random Forests are used in the verification experiments. Error rates comparable to the ones reported over the same signature datasets in a recent Signature Verification Competition, show the potential of the proposed approach. The experimental results, also show that there is a good correlation between the consistency factor and the verification errors, suggesting that consistency values could be used to select the optimal feature combination.