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15
Combining Match Scores with Liveness Values in a Fingerprint Verification System
"... We discuss the problem of combining biometric match scores with liveness measure values in the context of fingerprint verification. Recent literature has focused on the development of methods to assess if an input fingerprint sample is a “live ” entity or a “spoof ” artefact. This is commonly done b ..."
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We discuss the problem of combining biometric match scores with liveness measure values in the context of fingerprint verification. Recent literature has focused on the development of methods to assess if an input fingerprint sample is a “live ” entity or a “spoof ” artefact. This is commonly done by generating a singlevalued numerical entity referred to as the liveness measure value. However, the problem of combining this liveness value with match scores has not been rigorously investigated. The goal of this work is to design a framework in which a liveness detector is incorporated with a fingerprint matcher. We first design and analyze three different methods to combine match scores with liveness values. Next, we introduce a Bayesian Belief Network (BBN) scheme that models the relationship between match scores and liveness values. Experiments carried out on a publicly available database of the Fingerprint Liveness Detection Competition 2009 (LivDet09) show the effectiveness of assuming a certain degree of influence of liveness values on match scores. 1.
An independence of causal interactions model for opposing influences
 Proc. 4th European Workshop on Probabilistic Graphical Models
, 2008
"... We introduce the DeMorgan gate, an Independence of Causal Interactions (ICI) model that is capable of modeling opposing influences, i.e., a mixture of positive and negative influences of parents on a child. The model is a noisy version of a conjunctive normal form of Boolean functions and is an exte ..."
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We introduce the DeMorgan gate, an Independence of Causal Interactions (ICI) model that is capable of modeling opposing influences, i.e., a mixture of positive and negative influences of parents on a child. The model is a noisy version of a conjunctive normal form of Boolean functions and is an extension and a combination of the popular NoisyOR and NoisyAND models, preserving their intuitive semantics. We report the results of a simple experiment testing the usefulness of the proposed model for elicitation of conditional probability distributions. 1
Exploring the noisy threshold function in designing bayesian networks
 In Proceedings of SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
, 2005
"... Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of ..."
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Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at least one cause, or all causes together, give rise to an effect, however, seems unnecessarily restrictive. In the present paper a new, more flexible, causal independence model is proposed, based on the Boolean threshold function. A connection is established between conditional probability distributions based on the noisy threshold model and Poisson binomial distributions, and the basic properties of this probability distribution are studied in some depth. The successful application of the noisy threshold model in the refinement of a Bayesian network for the diagnosis and treatment of ventilatorassociated pneumonia demonstrates the practical value of the presented theory. 1
Symmetric causal independence models for classification
 In The third European Workshop on Probabilistic Graphical Models
, 2006
"... Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we propose an application of an extended class of causal independence models, causal independence models based on th ..."
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Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we propose an application of an extended class of causal independence models, causal independence models based on the symmetric Boolean function, for classification. We present an EM algorithm to learn the parameters of these models, and study convergence of the algorithm. Experimental results on the Reuters data collection show the competitive classification performance of causal independence models based on the symmetric Boolean function in comparison to noisy OR model and, consequently, with other stateoftheart classifiers. 1
Noisy threshold functions for modelling causal independence in Bayesian networks
, 2006
"... Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of ..."
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Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at least one cause, or all causes together, give rise to an effect, however, seems unnecessarily restrictive. In the present paper a new, more flexible, causal independence model is proposed, based on the Boolean threshold function. A connection is established between conditional probability distributions based on the noisy threshold model and Poisson binomial distributions, and the basic properties of this probability distribution are studied in some depth. We present and analyse recursive methods as well as approximation and bounding techniques to assess the conditional probabilities in the noisy threshold models.
EM Algorithm for Symmetric Causal Independence Models
"... Abstract. Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric B ..."
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Abstract. Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric Boolean function. The developed algorithm enables us to assess the practical usefulness of the symmetric causal independence models, which has not been done previously. We evaluate the classification performance of the symmetric causal independence models learned with the presented EM algorithm. The results show the competitive performance of these models in comparison to noisy OR and noisy AND models as well as other stateoftheart classifiers. 1
Modelling Regional Grazing. Viability in Outback Australia Using Bayesian Livelihood Networks
, 2007
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The Probabilistic Interpretation of ModelBased Diagnosis
"... Abstract. Modelbased diagnosis is the field of research concerned with the problem of finding faults in systems by reasoning with abstract models of the systems. Typically, such models offer a description of the structure of the system in terms of a collection of interacting components. For each of ..."
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Abstract. Modelbased diagnosis is the field of research concerned with the problem of finding faults in systems by reasoning with abstract models of the systems. Typically, such models offer a description of the structure of the system in terms of a collection of interacting components. For each of these components it is described how they are expected to behave when functioning normally or abnormally. The model can then be used to determine which combination of components is possibly faulty in the face of observations derived from the actual system. There have been various proposals in literature to incorporate uncertainty into the diagnostic reasoning process about the structure and behaviour of systems, since much of what goes on in a system cannot be observed. This paper proposes a method for decomposing the probability distribution underlying probabilistic modelbased diagnosis in two parts: (i) apart that offers a description of uncertain abnormal behaviour in terms of the Poissonbinomial probability distribution, and (ii) a part describing the deterministic, normal behaviour of system components. 1
Modelling the Interactions between Discrete and Continuous Causal Factors in Bayesian Networks
"... The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so f ..."
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The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modelled by means of causal independence theory, as a theory of continuous causal independence was not available. In this paper, such a theory is developed and generalised such that it allows merging continuous with discrete parameters based on the characteristics of the problem at hand. This new theory is based on the discovered relationship between the theory of causal independence and convolution in probability theory, discussed for the first time in this paper. It is also illustrated how this new theory can be used in connection with special probability distributions. 1
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, 2006
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