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Abstract
This paper proposes the biometric verification system based on ocular features. We form the multimodal biometric system considering two recent biometric traits in ocular region- sclera region and periocular region. For feature extraction of sclera part we use simple technique which eliminates the expensive image enhancement process i.e Local Binary Pattern (LBP) and the matching scores are generated. For feature extraction of periocular region we use structured random projections and matching score are generated. From these matching scores the score level fusion is done with Extreme Learning Machine (ELM). This method has shown 94.40% of accuracy.
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How to Cite
S.N. Dharwadkar, P. R. (2015). Fusion of Sclera and Periocular Features for Biometric System. International Journal of Emerging Trends in Science and Technology, 2(07). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/806
References
1. Z. Zhou, E. Y. Du, N.L. Thomas, “A comprehensive sclera image quality measureâ€, in Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore, pp. 638–643, 2010.
2. R. Derakhshani, A. Ross, S. Crihalmeanu, “A new biometric modality based on conjunctival vasculatureâ€, in Proceedings of Artificial Neural Networks in Engineering (ANNIE 2006), St. Louis, Missouri, USA, 2006.
3. S. Crihalmeanu, A. Ross, R. Derakhshani, “Enhancement and registration schemes for matching conjunctival vasculatureâ€, in: Proceedings of the 3rd IAPR/IEEE International Conference on Biometrics (ICB 2009), Italy, pp. 1240–1249, 2009.
4. R. Derakhshani, A. Ross, “A texture-based neural network classifier for biometric identification using ocular surface vasculatureâ€, in Proceedings of the International Joint Conference on Neural Networks (IJCNN 2007), Kensas, pp. 2982–2987, USA, 2007.
5. N.L. Thomas, Y. Du, Z. Zhou, “A new approach for sclera vein recognitionâ€, in Proceedings of the International Society for Optical Engineering (SPIE), vol. 7708, 2010.
6. Z. Zhou, E. Y. Du, N.L. Thomas, “A comprehensive sclera image quality measureâ€, in Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore, pp. 638–643, 2010.
7. S. Crihalmeanu, A. Ross, “Multispectral scleral patterns for ocular biometric recognitionâ€, Pattern Recognit. Lett., vol.33 (14), pp. 1860–1869, 2012.
8. U. Park, A. Ross, A.K. Jain, “Periocular biometrics in the visible spectrum: a feasibility studyâ€, in Proceedings of the 3rd International Conference on Biometrics: Theory, Application, and Systems (BTAS 2009), 2009.
9. J. Adams, D.L. Woodard, G. Dozier, P. Miller, K. Bryant, G. Glenn, “Genetic-based type II feature extraction for periocular biometric recognition: less is moreâ€, in: Proceedings of the 20th International Conference on Pattern Recognition (ICPR 2010), pp. 205–208, 2010.
10. J. Xu, M. Cha, J.L. Heyman, S. Venugopalan, R. Abiantun, M. Savvides, “Robust local binary pattern feature sets for periocular biometric identificationâ€, in: Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2010), 2010.
11. D. Woodard, S. Pundlik, P. Miller, R. Jillela, A. Ross, “On the fusion of periocularand iris biometrics in non-ideal imageryâ€, in Proceedings of the 20th International Conference on Pattern Recognition (ICPR 2010), pp. 201–204, 2010.
12. D.L. Woodard, S.J. Pundlik, P.E. Miller, J.R. Lyle, “Appearance-based periocular features in the context of face and non-ideal iris recognitionâ€, Signal Image Video Process, pp. 1–13, 2011.
13. G. Santos, E. Hoyle, “A fusion approach to unconstrained iris recognitionâ€, Pattern Recognit. Lett. vol. 33 (8), pp. 984–990 , 2012.
14. K. Oh, K.-A.Toh, “Extracting sclera features for cancelable identity verificationâ€, in: Proceedings of the 5th IAPR International Conference on Biometrics (ICB 2012), NewDelhi, India, 2012.
15. B.-S. Oh, K.-A. Toh, A.B.J. Teoh, J. Kim, “Combining local face image features identity veriï¬cationâ€, Neurocomputing vol. 74 (16), pp. 2452–2463, 2011.
16. G.-B. Huang, Q.-Y.Zhu, C.-K.Siew, “Extreme learning machine: theory and applicationsâ€, Neurocomputing, 70(1) pp. 489–501, 2011.
17. T. Ojala, M. Pietikäinen, T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classiï¬cation with local binary patternsâ€, in: IEEE Trans. Pattern Anal. Mach. Inte., vol. 24 (7), pp.- 971–987, 2002.
18. Hugo Proença, SÃlvio Filipe, Ricardo Santos, João Oliveira, LuÃs A. Alexandre, “The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-The-Move and At-A-Distanceâ€, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, (8), pag. 1529-1535, August, 2010.
19. Kangrok Oh, Beom-Seok Oh, Kar-Ann Toh, Wei-Yun Yau, How-Lung Eng, “Combining sclera and periocular features for multimodal identity verificationâ€, Neurocomputing, vol.128, pp. 185-198, 2014.
2. R. Derakhshani, A. Ross, S. Crihalmeanu, “A new biometric modality based on conjunctival vasculatureâ€, in Proceedings of Artificial Neural Networks in Engineering (ANNIE 2006), St. Louis, Missouri, USA, 2006.
3. S. Crihalmeanu, A. Ross, R. Derakhshani, “Enhancement and registration schemes for matching conjunctival vasculatureâ€, in: Proceedings of the 3rd IAPR/IEEE International Conference on Biometrics (ICB 2009), Italy, pp. 1240–1249, 2009.
4. R. Derakhshani, A. Ross, “A texture-based neural network classifier for biometric identification using ocular surface vasculatureâ€, in Proceedings of the International Joint Conference on Neural Networks (IJCNN 2007), Kensas, pp. 2982–2987, USA, 2007.
5. N.L. Thomas, Y. Du, Z. Zhou, “A new approach for sclera vein recognitionâ€, in Proceedings of the International Society for Optical Engineering (SPIE), vol. 7708, 2010.
6. Z. Zhou, E. Y. Du, N.L. Thomas, “A comprehensive sclera image quality measureâ€, in Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), Singapore, pp. 638–643, 2010.
7. S. Crihalmeanu, A. Ross, “Multispectral scleral patterns for ocular biometric recognitionâ€, Pattern Recognit. Lett., vol.33 (14), pp. 1860–1869, 2012.
8. U. Park, A. Ross, A.K. Jain, “Periocular biometrics in the visible spectrum: a feasibility studyâ€, in Proceedings of the 3rd International Conference on Biometrics: Theory, Application, and Systems (BTAS 2009), 2009.
9. J. Adams, D.L. Woodard, G. Dozier, P. Miller, K. Bryant, G. Glenn, “Genetic-based type II feature extraction for periocular biometric recognition: less is moreâ€, in: Proceedings of the 20th International Conference on Pattern Recognition (ICPR 2010), pp. 205–208, 2010.
10. J. Xu, M. Cha, J.L. Heyman, S. Venugopalan, R. Abiantun, M. Savvides, “Robust local binary pattern feature sets for periocular biometric identificationâ€, in: Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2010), 2010.
11. D. Woodard, S. Pundlik, P. Miller, R. Jillela, A. Ross, “On the fusion of periocularand iris biometrics in non-ideal imageryâ€, in Proceedings of the 20th International Conference on Pattern Recognition (ICPR 2010), pp. 201–204, 2010.
12. D.L. Woodard, S.J. Pundlik, P.E. Miller, J.R. Lyle, “Appearance-based periocular features in the context of face and non-ideal iris recognitionâ€, Signal Image Video Process, pp. 1–13, 2011.
13. G. Santos, E. Hoyle, “A fusion approach to unconstrained iris recognitionâ€, Pattern Recognit. Lett. vol. 33 (8), pp. 984–990 , 2012.
14. K. Oh, K.-A.Toh, “Extracting sclera features for cancelable identity verificationâ€, in: Proceedings of the 5th IAPR International Conference on Biometrics (ICB 2012), NewDelhi, India, 2012.
15. B.-S. Oh, K.-A. Toh, A.B.J. Teoh, J. Kim, “Combining local face image features identity veriï¬cationâ€, Neurocomputing vol. 74 (16), pp. 2452–2463, 2011.
16. G.-B. Huang, Q.-Y.Zhu, C.-K.Siew, “Extreme learning machine: theory and applicationsâ€, Neurocomputing, 70(1) pp. 489–501, 2011.
17. T. Ojala, M. Pietikäinen, T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classiï¬cation with local binary patternsâ€, in: IEEE Trans. Pattern Anal. Mach. Inte., vol. 24 (7), pp.- 971–987, 2002.
18. Hugo Proença, SÃlvio Filipe, Ricardo Santos, João Oliveira, LuÃs A. Alexandre, “The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-The-Move and At-A-Distanceâ€, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, (8), pag. 1529-1535, August, 2010.
19. Kangrok Oh, Beom-Seok Oh, Kar-Ann Toh, Wei-Yun Yau, How-Lung Eng, “Combining sclera and periocular features for multimodal identity verificationâ€, Neurocomputing, vol.128, pp. 185-198, 2014.