Fundamentals of speech recognition
Fundamentals of speech recognition
ACM Computing Surveys (CSUR)
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Analysis of Stability in Hand-Written Dynamic Signatures
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
On-Line Signature Verification with Hidden Markov Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A novel criterion for writer enrolment based on a time-normalized signature sample entropy measure
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Forgery Quality and Its Implications for Behavioral Biometric Security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On Using the Viterbi Path Along With HMM Likelihood Information for Online Signature Verification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new forgery scenario based on regaining dynamics of signature
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Evaluating the biometric sample quality of handwritten signatures
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
| Hi-index | 0.01 |
This work proposes a novel measure to quantify the quality of a skilled forgery sample in the online signature framework. Such a quality measure is constructed by adapting our former Personal Entropy to the context of skilled forgeries production. For validation, we confront our quality measure to several types of skilled forgeries (static, dynamic, professional) captured on different acquisition platforms. Indeed, four databases are exploited: MCYT-100, Philips database, BioSecure data subsets DS2 and DS3. We prove the effectiveness of our quality measure to quantify the quality of all types of skilled forgeries available with regards to the performance of three classifiers: a Dynamic Time Warping, a Hidden Markov models and a Gaussian Mixture Model.