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Chaotic Neural Networks and Multi-Dimensional Data Analysis in Biometric Applications

Chaotic Neural Networks and Multi-Dimensional Data Analysis in Biometric Applications. Presented by: Kushan Ahmadian Department of Computer Science, University of Calgary kahmadia@ucalgary.ca. Outline. Introduction Research Contributions Motivations Background Research Neural Network

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Chaotic Neural Networks and Multi-Dimensional Data Analysis in Biometric Applications

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  1. Chaotic Neural Networks and Multi-Dimensional Data Analysis in Biometric Applications Presented by: Kushan Ahmadian Department of Computer Science, University of Calgary kahmadia@ucalgary.ca

  2. 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

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

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

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

  6. Biometrics 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 Source: http://360biometrics.com/

  7. Multi-modal biometric Examples of fusion methods.

  8. Feature Space and Dimensionality Reduction 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions • Transform the data in the high-dimensional space to a space of fewer dimensions. 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 Böhm, 2007).

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

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

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

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

  14. Eigenface images • 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)

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

  16. Reducing Dimensionality by Subspace Analysis 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions • Three points p1, p2, p3 on a plane (b) Corresponding parameterization functions.

  17. Reducing Dimensionality by Subspace Analysis • Find the clusters within an error range of ε. • 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 << x*y Next, we try to learn the pattern using a learner (Chaotic Neural Network) 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

  18. Associative Memory 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions • The neuron signals comprise an output pattern. • The neuron signals are initially set equal to some input pattern. • The network converges to the nearest stored pattern

  19. Chaotic Associative Memory • Chaotic and period doubling noise injection policies • To overcome the drawback of non-autonomous methods is their blind noise-injecting strategy • Proposing the adjacency matrix to evaluate the chances of a neuron to receive chaotic noise 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

  20. Fingerprint Neural Based Method – Case Study 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions The general goal is to train the network using the Delaunay triangulation of minutiae points.

  21. DT based Matching -Experimental Results 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

  22. Multimodal Training Phase 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions User 1 User 2 User N Data acquisition • Biometric3 • Biometric2 • Biometric1 Analyzing and obtaining the best set of feature vectors Training the chaotic associative memory with the obtained vectors

  23. User Testing Data acquisition Biometric3 Biometric2 Biometric1 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions Dimensionality reduction – New feature space User’s obtained feature space vector Feeding the new vector space into the associative memory Network Convergence (Matching) Yes/ No

  24. Experimental Results • My method: Subspace Clustering (SC) and Chaotic Noise Neural Network (CNNN) • Compared methods: • Simple-Sum (SS), Min-Score (MIS), Max-Score (MAS), Matcher Weighting (MW), User Weighting (UW) • Min-Max Score (MM), Zero Score (ZS), Tanh (TH), Quadratic Line Quadric (QLQ), Subspace Clustering (SC) 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

  25. Experimental Results 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions EER rate, SC with different fusion techniques EER rate, CNN with different normalization techniques

  26. Experimental Results 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions EER rate, Combination of different fusion and normalization techniques

  27. Conclusions • The contributions are : • Introducing method for selecting a proper set of input features to reduce the dimensionality of biometric data and consequently enhancing the performance of the system. • Introducing chaotic associative memories in biometric system, which have significant advantages over conventional memories in terms of capacity of the memory • Implementing and enhancing performance of the biometric multimodal verification system. 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

  28. Future Work • Continuing research on the axis-parallel subspace clustering. • Comparing to a newly proposed system where the data analysis is run over the vectors of each biometric separately. The benefit of such a system would being more tolerable over the absence of each biometric. • Enhancing the capacity of the associative memories which is the current drawback of associative based memories. • Finding a better candidate vector for subspace clustered data to improve the quality of data reduction method. • Continuing research on subspace clustering methods to further decrease FAR and FRR rates 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

  29. Project Timeline 1 Introduction 2 Background 3 Methodology 4 Experiments 5. Conclusions

  30. Key References • Bohm C, Kailing K, Kriegel H.P, Kroger P, (2004) Density Connected Clustering with Local Subspace Preferences, Proceedings of the Fourth IEEE International Conference on Data Mining, p.27-34, • Jain A. K., Ross A., and Prabhakar A. (2004) An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, 14(1):4–20. • Kriegel H. P, Kröger P, Zimek Z. (2009) Clustering High Dimensional Data: A Survey on Subspace Clustering, Pattern-based Clustering, and Correlation Clustering • ACM Transactions on Knowledge Discovery from Data pp.1-58, • Wang L, and Shi H (2006) A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks. IEEE Transactions on neural networks, vol 17, no. 4:989- 1001 • Zhao L. and Yang Y. H., (1999) “Theoretical Analysis of Illumination in PCA-Based Vision Systems,” Pattern Recognition, Vol. 32, No. 4, pp.547-564. • Belhumeur P. N, Hespanha J. P, and Kriegman D. J. (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp.711-720.

  31. Publications • K.Ahmadian and M. Gavrilova, “Transiently Chaotic Associative Network for Fingerprint Image Analysis”, Special Issue on Intelligent Computing for Multimedia Assurance in the  International Journal of Neural Network World, A. Abraham editor, 2009-2010, 21 pages, in print (accepted in May 2009) • K.Ahmadian and M. Gavrilova “On-Demand Chaotic Neural Network for Broadcast Scheduling Problem”, Journal of Supercomputing, 18 pages, Springer ( accepted with minor revisions in May 2010). • K. Ahmadian, and M. Gavrilova, “Multi-objective Evolutionary Approach for Biometric Fusion,” IEEE International Conference on Biometrics and Kansei Engineering, pp. 12-17, June 25-28, Poland, 2009. • K. Ahmadian, M. Gavrilova and D. Taniar, “Multi-criteria Optimization in GIS: Continuous K-Nearest Neighbor search in mobile navigation,” ICCSA, pp.574-589, March 2010, Japan.

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