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Presented by: Kushan Ahmadian Department of Computer Science, University of Calgary kahmadia@ucalgary.ca 1

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Outline Introduction Research Contributions Motivations Background Research Neural Network Dimensionality Reduction Biometrics Proposed Methodology Subspace Clustering Chaotic Associative Memory Overall System Architecture Preliminary Experimental Results Conclusion and Future Work 2

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Research Goal The 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. 3 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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My Research Contributions A 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 results 4 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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Motivation Alleviate 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. 5 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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Biometrics 6 Source: http://360biometrics.com/ Biometrics comprises methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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Multi-modal biometric 7 Matchers FusedAuthorsLevel of FusionFusion methodology HandKumar et al (2003)Feature, Match score Feature concatenation/ sum rule Pulmprint (geometry, local texture) You, et al (2004)DecisionHierarchical matching Fingerprint(2 impressions) Jain and Ross (2002)Sensor, feature Mosaicing of templates FingerprintWilson et al (2004)Match scoreSum rule Face (global and local features) Ferrez et al (2005)Feature levelFeature concatenation VoiceCheung et al(2004)Match scoreZero sum fusion Face, Iris and Signature Gavrilova and Monwar (2009) Rank LevelMarkov model Examples of fusion methods.

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8 Matchers FusedAuthorsLevel of FusionFusion methodology HandKumar et al (2003)Feature, Match score Feature concatenation/ sum rule Pulmprint (geometry, local texture) You, et al (2004)DecisionHierarchical matching Fingerprint(2 impressions) Jain and Ross (2002)Sensor, feature Mosaicing of templates FingerprintWilson et al (2004)Match scoreSum rule Face (global and local features) Ferrez et al (2005)Feature levelFeature concatenation VoiceCheung et al(2004)Match scoreZero sum fusion Face, Iris and Signature Gavrilova and Monwar (2009) Rank LevelMarkov model

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Feature Space and Dimensionality Reduction Transform the data in the high-dimensional space to a space of fewer dimensions. 9 Subspace obtained by PCA and ideal resulted subspace projected clustering (Han and Kamber, 2001) DBSCAN (Ester et.al. 1996) Specifications of clustering methods (Achtert and Bhm, 2007). 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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Reducing Dimensionality by Subspace Analysis The 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

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Neural network Chaotic Neural Networks un pattern Rec.(Wang, 2006) CSA (Chen and Aihara, 1997) Applications of Optimization (Wang, 1998) 11 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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Traditional System Architecture 12 Traditional multimodal architecture 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions Biometric Database Eigenfaces vectors PCA-based dimensionality reduction User samples Yes/No Learner 1 Aggregation method

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Proposed System Architecture 13 Proposed biometric recognition system Biometric Database User samples Mean faces Novel representation of Feature Vector Train neural networks Testing neural network Yes/No Verified ? Train ? N Y 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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Subspace Clustering Step 1 14 Mean image for each class For each person (class) compute the mean image 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions Input Data

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Eigenface images 15 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)

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Subspace Clustering Step 2 16 Number of dimensions: m (number of mean images) Number of points in the high dimensional space: x*y 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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17 Three points p 1, p 2, p 3 on a plane (b) Corresponding parameterization functions. Reducing Dimensionality by Subspace Analysis 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

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Reducing Dimensionality by Subspace Analysis Find 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