Fundamentals of digital image processing
Fundamentals of digital image processing
Signature recognition through spectral analysis
Pattern Recognition
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
Visual Identification by Signature Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line Handwritten Signature Verification using Hidden Markov Model Features
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Guide to Biometrics
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Adapted user-dependent multimodal biometric authentication exploiting general information
Pattern Recognition Letters
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
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)
Spectrum Analysis Based onWindows with Variable Widths for Online Signature Verification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
On-line signature recognition based on VQ-DTW
Pattern Recognition
An efficient face verification method in a transformed domain
Pattern Recognition Letters
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
HMM-based on-line signature verification: Feature extraction and signature modeling
Pattern Recognition Letters
A novel local on-line signature verification system
Pattern Recognition Letters
Pattern Recognition
Score normalization in multimodal biometric systems
Pattern Recognition
An on-line signature verification system based on fusion of local and global information
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Target dependent score normalization techniques and their application to signature verification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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
Novel algorithm for the on-line signature verification
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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In this paper we introduce a new biometric verification system based on on-line signatures and simulate its operation. For this purpose we have split the MCYT signature database in three subsets: one for classifier training, another for system adjustment and a third one for system testing simulating enrolment and verification. This context corresponds to a real operation, where a new user tries to enrol an existing system and must be automatically guided by the system in order to detect the failure to enrol (FTE) situations. The main contribution of this work is the management of FTE situations by means of a new proposal, called intelligent enrolment, which consists of consistency checking in order to automatically reject low quality samples. This strategy enhances the performance of the system to 22% when 8% of the users are left out. In this situation 8% of the people cannot be enroled in the system and must be verified by other biometrics or by human abilities. These people are identified with intelligent enrolment and the situation can be thus managed. In addition we also propose a DCT-based feature extractor with threshold coding and discriminability criteria.