Publication

Gaussian mixture models for on-line signature verification

Andrzej Drygajlo
2003
Conference Papers

Abstract

This paper introduces and motivates the use of Gaussian Mixture Models (GMMs) for on-line signature verification. The individual Gaussian components are shown to represent some local, signer-dependent features that characterise spatial and temporal aspects of a signature, and are effective for modelling its specificity. The focus of this work is on automated order selection for signature models, based on the Minimum Description Length (MDL) principle. A complete experimental evaluation of the Gaussian Mixture signature models is conducted on a 50-user subset of the MCYT multimodal database. Algorithmic issues are explored and comparisons to other commonly used on-line signature modelling techniques based on Hidden Markov Models (HMMs) are made.

Official source

https://infoscience.epfl.ch/entities/publication/bf1e7c9f-ef8b-42ab-ac49-7c779d496678

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Andrzej Drygajlo
2003
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/bf1e7c9f-ef8b-42ab-ac49-7c779d496678

About this result

Related concepts (28)

Markov model

In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property.

Digital signature

A digital signature is a mathematical scheme for verifying the authenticity of digital messages or documents. A valid digital signature on a message gives a recipient confidence that the message came from a sender known to the recipient. Digital signatures are a standard element of most cryptographic protocol suites, and are commonly used for software distribution, financial transactions, contract management software, and in other cases where it is important to detect forgery or tampering.

Mixture model

In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population.

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