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Chaotic Neural Networks and Multi-Dimensional Data Analysis in Biometric ApplicationsPresented by: Kushan Ahmadian

Department of Computer Science, University of Calgarykahmadia@ucalgary.ca

1OutlineIntroductionResearch ContributionsMotivationsBackground ResearchNeural NetworkDimensionality ReductionBiometricsProposed MethodologySubspace ClusteringChaotic Associative MemoryOverall System ArchitecturePreliminary Experimental ResultsConclusion and Future Work

22Research GoalThe purpose of my research is to develop a novel methodology based on the subspace clustering dimension reduction technique and chaotic neural network to improve the performance and circumvention of multi-modal biometric system. 31 Introduction2 Background3 Methodology 4 Experiments5. ConclusionsMy Research ContributionsA novel correlation clustering approach accounting for the feature relevance and/or feature correlation problem in multi-modal biometric system

Design and utilization of a chaotic associative neural memory with original noise injection policy to learn the patterns of biometric features

Designing and evaluating the performance of the system comparing the results to the post-classification (decision level) fusion results41 Introduction2 Background3 Methodology 4 Experiments5. ConclusionsMotivationAlleviate problems of current dimensionality reduction methods such as curse of dimensionality and locality by proposing a new subspace clustering based dimensionality reduction for biometric data.Reducing the FAR (False Acceptance Rate) and FRR (False Rejection Rate) by minimizing the effect of noise, template aging and other errors using a novel feature selection method.Utilizing a brain-like associative memory (chaotic neural network) for the first time in biometric to enhance the ability of pattern-based data retrieval from memory.

51 Introduction2 Background3 Methodology 4 Experiments5. ConclusionsBiometrics6

Source: http://360biometrics.com/Biometrics comprises methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits.

1 Introduction2 Background3 Methodology 4 Experiments5. Conclusions

Multi-modal biometric7Matchers FusedAuthorsLevel of FusionFusion methodologyHandKumar et al (2003)Feature, Match scoreFeature concatenation/ sum rulePulmprint (geometry, local texture)You, et al (2004)DecisionHierarchical matchingFingerprint(2 impressions)Jain and Ross (2002)Sensor, featureMosaicing of templatesFingerprintWilson et al (2004)Match scoreSum ruleFace (global and local features)Ferrez et al (2005)Feature levelFeature concatenationVoiceCheung et al(2004)Match scoreZero sum fusionFace, Iris and SignatureGavrilova and Monwar (2009)Rank LevelMarkov modelExamples of fusion methods.8Matchers FusedAuthorsLevel of FusionFusion methodologyHandKumar et al (2003)Feature, Match scoreFeature concatenation/ sum rulePulmprint (geometry, local texture)You, et al (2004)DecisionHierarchical matchingFingerprint(2 impressions)Jain and Ross (2002)Sensor, featureMosaicing of templatesFingerprintWilson et al (2004)Match scoreSum ruleFace (global and local features)Ferrez et al (2005)Feature levelFeature concatenationVoiceCheung et al(2004)Match scoreZero sum fusionFace, Iris and SignatureGavrilova and Monwar (2009)Rank LevelMarkov modelFeature Space and Dimensionality ReductionTransform the data in the high-dimensional space to a space of fewer dimensions.

9Subspace obtained by PCA and ideal resulted subspaceprojected clustering (Han and Kamber, 2001)DBSCAN (Ester et.al. 1996)Specifications of clustering methods (Achtert and Bhm, 2007).1 Introduction2 Background3 Methodology 4 Experiments5. ConclusionsReducing Dimensionality by Subspace AnalysisThe principle for subspace analysis is based on a generalized description of spherical coordinates.

A point in data space is represented by a sinusoidal curve in parameter space P.

A point in parameter space corresponds to a (d 1)-dimensional hyperplane in data space.10

Neural networkChaotic Neural Networks un pattern Rec.(Wang, 2006)CSA (Chen and Aihara, 1997)Applications of Optimization (Wang, 1998)11

1 Introduction2 Background3 Methodology 4 Experiments5. ConclusionsTraditional System Architecture12Traditional multimodal architecture1 Introduction2 Background3 Methodology 4 Experiments5. ConclusionsBiometric DatabaseEigenfaces vectorsPCA-based dimensionality reductionUser samplesYes/NoLearner 1Learner 1Learner 1Aggregation methodProposed System Architecture13Proposed biometric recognition systemBiometric DatabaseUser samplesMean facesNovel representation of Feature VectorTrain neural networksTesting neural networkYes/NoVerified?Train?NY1 Introduction2 Background3 Methodology 4 Experiments5. ConclusionsSubspace Clustering Step 114

Mean image for each class

For each person (class) compute the mean image1 Introduction2 Background3 Methodology 4 Experiments5. ConclusionsInput DataEigenface images15

The eigenvectors are sorted in order of descending eigenvalues and the greatest eigenvectors are chosen to represent face space.This reduces the dimensionality of the image space, yet maintains a high level of variance between face images throughout the image subspace.Any face image can then be represented as a vector of coefficients, corresponding to the contribution of each eigenface.Each eigenvector can be displayed as an image and due to the likeness to faces (FERET database)Subspace Clustering Step 216

Number of dimensions: m (number of mean images)Number of points in the high dimensional space: x*y

1 Introduction2 Background3 Methodology 4 Experiments5. Conclusions

17Three points p1, p2, p3 on a plane (b) Corresponding parameterization functions.Reducing Dimensionality by Subspace Analysis1 Introduction2 Background3 Methodology 4 Experiments5. Conclusions

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Reducing Dimensionality by Subspace AnalysisFind the clusters within an error range of .Use the mean vector as the candidate for the members of a cluster and create the new vector space. The number of points of the new space is:

M