Elements of information theory
Elements of information theory
Fundamentals of speech recognition
Fundamentals of speech recognition
The shape of handwritten characters
Pattern Recognition Letters
Selection of Reference Signatures for Automatic Signature Verification
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
An HMM On-line Signature Verifier Incorporating Signature Trajectories
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Gaussian Mixture Models for on-line signature verification
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
On-Line Signature Verification with Hidden Markov Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Identity authentication using improved online signature verification method
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
On Using the Viterbi Path Along With HMM Likelihood Information for Online Signature Verification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
On measuring forgery quality in online signatures
Pattern Recognition
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This paper proposes a novel criterion for an improved writer enrolment based on an entropy measure for online genuine signatures. As online signature is a temporal signal, we measure the time-normalized entropy of each genuine signature, namely, its average entropy per second. Entropy is computed locally, on portions of a genuine signature, based on local density estimation by a Client-Hidden Markov Model. The average time-normalized entropy computed on a set of genuine signatures allows then categorizing writers in an unsupervised way, using a K-Means algorithm. Linearly separable and visually coherent classes of writers are obtained on MCYT-100 database and on a subset of BioSecure DS2 containing 104 persons (DS2-104). These categories can be analyzed in terms of variability and complexity measures that we have defined in this work. Moreover, as each category can be associated with a signature prototype inherited from the K-Means procedure, we can generalize the writer categorization process on the large subset DS2-382 from the same DS2 database, containing 382 persons. Performance assessment shows that one category of signatures is significantly more reliable in the recognition phase, and given the fact that our categorization can be used online, we propose a novel criterion for enhanced writer enrolment.