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
On-line Handwritten Signature Verification using Hidden Markov Model Features
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A comparative study on the consistency of features in on-line signature verification
Pattern Recognition Letters
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
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Effectiveness of pen pressure, azimuth, and altitude features for online signature verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
On-line signature verification using vertical signature partitioning
Expert Systems with Applications: An International Journal
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Online signature verification is used widely for user authentication in many applications. In existing systems, one confidential level is usually used as the threshold for signature verifications. This causes high false rejection ratio (FRR) to systems in which signature verification is less important, and on the other hand, high false acceptance ratio (FAR) to systems in which signature verification is more critical. Thus, applying multi-confidential levels to the signature verification is crucial to solve this problem. Also, data mining techniques can be used to appropriately define multi-confidential levels. This paper proposes a hybrid online signature verification system supporting multi-confidential levels defined by data mining techniques.