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A Fast IRIS Recognition Technique Based on Dimensionality Optimization and Multidomain Feature Normalization

V. V. Satyanarayana Tallapragada, Dr. E. G. Rajan

Abstract


Biometric Solutions are the need of hour for future security based technique. IRIS is considered as one of the better biometric models for representing a human Identity due to invariability with age. Recognition efficiency in IRIS Recognition technique depends upon number of features. Larger Features affects the space complexity and computational complexity of the Technique. Hence in this work we propose a unique scalable IRIS recognition technique to define a feature vector by using the descriptor of different IRIS identities like Shape, Color, Texture Frequency and Phase and proposed a mechanism for reducing the feature space by first normalizing the feature values and then subsequently reducing it by log likelihood dimensionality reduction technique aligned with PCA method. Further the reduced dimensions are re-optimized with Koghnen‘s Self Organizing Maps to represent the feature vectors in two dimensional feature space with fixed range in each dimension for ease of storage. Experiments are conducted over Noisy MMU Iris Database and Phoenix Iris Database to analyze the performance. Results show that the features extracted and optimized by the proposed technique gives an average accuracy of 98.6 %.

Keywords


IRIS, Koghnen‘s Map, Self Organizing Map, Neural Network, Wavelet, GLCM, Shape Descriptors, Fourier Transform, Color Features, Dimensionality Reduction, PCA, LDA.

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References


Jing Luo, Shuzhong Lin, Ming Lei, Jianyun Ni, "Application of Dimensionality Reduction Analysis to Fingerprint Recognition," iscid, vol. 2, pp.102-105, 2008 International Symposium on Computational Intelligence and Design, 2008.

Wen-Shiung Chen, Chi-An Chuan, Sheng-Wen Shih, Shun-Hsun Chang, "Iris recognition using 2D-LDA + 2D-PCA," icassp, pp.869-872, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009.

Tenenbaum, J. B., de Silva, V., , & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319–2323.

B. Sch¨olkopf, A. Smola, and K.-R. M¨uller. Kernel principal component analysis. In B. Sch¨olkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods — Support Vector Learning. MIT Press, Cambridge, MA, 1999b. 327 – 352.

S. Lespinats, M. Verleysen, A. Giron, and G. Fertil, ―DD-HDS: A method for visualization and exploration of high-dimensional data,‖ IEEE Trans. Neural Netw., vol. 18, no. 5, pp. 1265–1279, Sep. 2007.

Zhou Zhiping, Hui Maomao and Sun Ziwen, ―An iris recognition method based on 2DWPCA and neural network‖. Chinese Control and Decision Conference (CCDC '09), 17-19 June 2009, pp. 2357 – 2360.Digital Object Identifier 10.1109/CCDC.2009.5192124.

Caitang Sun, Farid Melgani, De Natale Francesco, Chunguang Zhou, Libiao Zhang, Xiaohua Liu, "Incremental Learning Based Color Iris Recognition," micai, pp.319-324, 2008 Seventh Mexican International Conference on Artificial Intelligence, 2008.

Caitang Sun, Farid Melgani, Chunguang Zhou, De Natale Francesco, Libiao Zhang, Xiangdong Liu, "Semi-Supervised Learning Based Color Iris Recognition," icnc, vol. 4, pp.242-249, 2008 Fourth International Conference on Natural Computation, 2008.

Ryszard S. Choras, "Iris Image Recognition," cisim, pp.26-30, 2007 6th International Conference on Computer Information Systems and Industrial Management Applications, 2007.

Haishan Chen, Xuan Han, Bochao Hu, "An Image Retrieval Method Based on Spatial Features of Colors," iscsct, vol. 1, pp.284-287, 2008 International Symposium on Computer Science and Computational Technology, 2008.

Hasan Demirel, Gholamreza Anbarjafari, "Iris Recognition System Using Combined Colour Statistics", IEEE Symposium on Signal Processing and Information Technology (ISSPIT 2008), Sarajevo, Bosnia, Dec. 2008, pp. 175-179.

N. Sudha, N. B. Puhan, H. Xia, and X. Jiang, ―Iris recognition on edge maps,‖ IET Computer Vision, vol. 3, no. 1, pp. 1–7, 2009.

S. Bhat and M. Savvides. Evaluating active shape models for eyeshape classification. In IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP2008), pages 5228–5231, 2008.

Ma Zheng, Zheng Tao and Pan Lili, ― Classification – Based Algorithms for Iris Outer Edge Location‖,IEEE 2009.

Yanyun Zhao, Anni Cai,‖ A Novel Relative Orientation Feature for Shape-Based Object Recognition‖, Proceedings of IC-NIDC, pp. 686-689, 2009.

Manjunath G, M. Naresh, V.V.Satyanarayana Tallapragada, ―A Novel Shape Recognition Technique by Shape Context and Zernike Moments for Content Based 3-D Object Retrieval System‖, International Conference on Systemics, Cybernetics, and Informatics (ICSCI 2011), pp. January, 2011.

V.V. Satyanarayana Tallapragada, E.G. Rajan, "Iris Recognition Based on Combined Feature of GLCM and Wavelet Transform," iciic, pp.205-210, 2010 First International Conference on Integrated Intelligent Computing, 2010.

Li Ma, Tieniu Tan, Yunhong Wang, and Dexin Zhang, ― Personal Identification Based on Iris Texture Analysis‖, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 12, pp. 1519-1533, 2003.

Fares S. Al-Qunaieer and Lahouari Ghouti, ― Color Iris Recognition Using Hypercomplex Gabor Wavelets‖, Symposium on Bio-inspired Learning and Intellingent Systems for Security, pp.18-19, 2009.

Li Ma, Tieniu Tan, Yunhong Wang, and Dexin Zhang, ― Personal Identification Based on Iris Texture Analysis‖, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, No. 12, pp. 1519-1533, 2003.

S. Liu, H. Yi, L.T. Chia, and D. Rajan. Adaptive hierarchical multi-class svm classifier for texture-based image classification. Proc. IEEE International Conference on Multimedia and Expo, page 1-4, 2005.

R. Y. F. Ng, Y. H. Tay, and K. M. Mok, ―Iris Recognition Algorithms Based on Texture Analysis,‖ Proc. 3rdInternational Symposium on Information Technology, vol. 2, Aug 2008, pp. 904-908.

Kamil Grabowski, Mariusz Zubert, Ma_gorzata Napieralska, Andrzej Napieralski, ―Uncertainty in Iris Recognition Based on Texture Analysis‖, 17th International Conference Mixed Design of Integrated Circuits and Systems (MIXDES 2010), pp. 587-592, 2010.

J. G. Daugman, ―High confidence visual recognition of persons by a test of statistical independence,‖ IEEE Trans. Pattern Anal.Mach. Intell., vol. 15, no. 11, pp. 1148–1161, Nov. 1993.

Randy P. Broussard, Lauren R. Kennell, David L. Soldan, and Robert W. Ives, ―Using Artificial Neural Networks and Feature Saliency Techniques for Improved Iris Segmentation‖, Proceedings of Internatioanl Joint Conference on Neural Networks, 2007.

Marcelo Mottalli, Marta Mejail and Julio Jacobo-Berlles, ―Flexible Image Segmentation and Quality Assessment for Real-Time Iris Recognition‖, Proceedings of International Conference on Image Processing(ICIP), 2009.

E. Levina and P.J. Bickel. Maximum likelihood estimation of intrinsic dimension. In Advances in Neural Information Processing Systems, volume 17, Cambridge, MA, USA, 2004. The MIT Press.

A.P. Papli´nski, "Neuro-Fuzzy Comp. — Ch. 8", pp. 8.1-8.13, May 12, 2005.

Dobeš, M., Martinek J., Skoupil D., Dobešová Z., Pospíšil J., Human eye localization using the modified Hough transform. Optik, Volume 117, No.10, p.468-473, Elsevier 2006, ISSN 0030-4026.

Dobeš M., Machala L., Tichavský P., Pospíšil J., Human Eye Iris Recognition Using the Mutual Information. Optik Volume 115, No.9, p.399-405, Elsevier 2004, ISSN 0030-4026.

Michal Dobeš and Libor Machala, Iris Database, http://www.inf.upol.cz/iris/

Kaushik Roy, Prabir Bhattacharya and Ramesh Chandra Debnath, ―Multi-Class SVM Based Iris Recognition‖, 10th International Conference on Computer and information technology, 2007.


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