The nature of statistical learning theory
The nature of statistical learning theory
Fractional Splines and Wavelets
SIAM Review
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Identity authentication using improved online signature verification method
Pattern Recognition Letters
Combining different biometric traits with one-class classification
Signal Processing
SVMC: single-class classification with support vector machines
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Combined handwriting and speech modalities for user authentication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Encyclopedia of Biometrics
Rough set approach to online signature identification
Digital Signal Processing
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In this work, a new set of features is presented for a biometric system based on speech and on-line signature. The feature vector is nonhomogeneous and it comprises using TESPAR DZ coefficients, wavelet energy coefficients and also some additional features resulted from the time domain analysis in the case of speech. A feature selection procedure is then applied to reduce the feature vector dimension. A modified symbols alphabet for the TESPAR DZ method is presented. Experimental results were reported using the SVC2004 database for signature and our own bimodal database BimDB10 (for on-line signature and speech). A feature level fusion strategy was adapted in order to achieve our goals. The results show that the fusion of biometric features brings improvement to the system performance.