Machine Learning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Gaussian Mixture Models for on-line signature verification
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Local and Global Feature Selection for On-line Signature Verification
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
HMM-based on-line signature verification: Feature extraction and signature modeling
Pattern Recognition Letters
Promoting Diversity in Gaussian Mixture Ensembles: An Application to Signature Verification
Biometrics and Identity Management
Statistical models of reconstructed phase spaces for signal classification
IEEE Transactions on Signal Processing - Part I
On Using the Viterbi Path Along With HMM Likelihood Information for Online Signature Verification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved model and feature set for signature recognition
ICCC'11 Proceedings of the 2011 international conference on Computers and computing
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Multivariate time series are sequences, whose order is provided by a time index; thus, most classifiers used on such data treat time as a special quantity, and encode it structurally in a model. A typical example of such models is the hidden Markov model, where time is explicitely used to drive state transitions. The time information is discretised into a finite set of states, the cardinality of which is largely chosen by empirical criteria. Taking as an example task signature verification, we propose an alternative approach using static probabilistic models of phase spaces, where the time information is preserved by embedding of the multivariate time series into a higher-dimensional subspace, and modelled probabilistically by using the theoretical framework of static Bayesian networks. We show empirically that performance is equivalent to state-of-the-art signature verification systems.