Comparative Analysis of Machine Learning and Clustering Based Algorithm for the classification of Ear Biometric Template

Volume: 3    Issue: 2

Year of Publication: 2016

Pages: (62-68)

Authors: Beant Kaur, Bhawana Kansal

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Abstract

Ear is a standout amongst the most solid biometric feature because of its steadiness and haphazardness. In this paper, we have developed a system that can recognize human ear patterns and comparative analysis of the results is done. A novel mechanism has been used for implementation of the system. Feature training has been used to extract the most discriminating features of the ear and is done using k –means clustering scheme. And finally the biometric templates are matched using neural network and centroid method which tells us whether the two ear images are same or not and on the basis of that performance metric are evaluated like error rate and Accuracy. The whole simulation is taken place in the MATLAB environment.

References

1. Burge, M., Burger, W., “Ear biometrics in computer vision,” In Proceedings of 15th International Conference on Pattern Recognition, 2000: 822-826W.

2. E.B. Baum and D. Haussler, ``What size net gives valid generalization?,\"\" Neural Computation, vol. 1, no. 1, pp. 151-160, 1989.

3. Jeff Heaton, Heaton Research, Inc. (25 November 2005)”, Introduction to neural networks with java,” ISBN-10: 097732060X, ISBN-13:978-0977320608.

4. Mark Burge and Wilhelm Burger. “Ear biometrics in computer vision”. In Proceedings of International Conference on Pattern Recognition (ICPR\" 00), volume 2, pages 822-826, 2000.

5. David J. Hurley, Mark S. Nixon, and John N. Carter. “Force Field feature extraction for ear biometrics”. Computer Vision and Image Understanding, 98(3):491-512, 2005.

6. Ping Yan and KevinW. Bowyer. “Empirical evaluation of advanced ear biometrics”. In Proceedings of International Conference on Computer Vision and Pattern Recognition-Workshop, volume 3, pages 41-48, 2005.

7. L. Alvarez, E. Gonzalez, and L. Mazorra. “Fitting ear contour using an ovoid model”. In Proceedings of IEEE International Carnahan Conference on Security Technology (ICCST\" 05), pages 145-148, 2005.

8. Ping Yan and K.W. Bowyer. “Biometric recognition using 3D ear shape”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8):1297-1308, 2007.

9. Saeeduddin Ansari and Phalguni Gupta, “Localization of ear using outer helix curve of the ear”. In Proceedings of the International Conference on Computing: Theory and Applications (ICCTA\" 07), pages 688-692, 2007.

10. Li Yuan and Zhi-Chun Mu. “Ear detection based on skin-color and contour information”. In Proceedings of International Conference on Machine Learning and Cybernetics (ICMLC\" 07), volume 4, pages 2213{2217, 2007.

11. Moreno, B., Sánchez, Á., Vélez. J.F. On the Use of Outer Ear Images for Personal Identification in Security Applications. IEEE 33rd Annual International Carnahan Conference on Security Technology, 1999, pp. 469- 476.

12. Victor, B., Bowyer, K., Sarkar, S. An evaluation of face and ear biometrics in Proceedings of International Conference on Pattern Recognition, pp. 429-432, August 2002.

13. Jain, A., Hong, L., Pankati, S. Biometric Identification Communications of the ACM, February 2000/Vol. 43, No. 2, pp. 91-98.

14. Burge, M. and Burger, W. Ear Biometrics in Computer Vision. In the 15th International Conference of Pattern Recognition, ICPR 2000, pp. 826-830.

15. Chang, K., Bowyer. K.W., Sarkar, S., Victor, B. Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, September 2003, pp. 1160-1165.

16. Hurley, D.J., Nixon, M. S., Carter, J.N. Automated Ear Recognition by Force Field Transformations in Proceedings IEE Colloquium: Visual Biometris (00/018), 2000a, pp. 8/1-8/5.

17. H.P. Narkhede, “Review of Image Segmentation Techniques, “International Journal of Science and Modern Engineering (IJISME), vol.1, pp. 54-61, July 2013.

18. Shalove Agarwal, Shashank Yadav and Kanchan Singh, “K-mean versus k-mean++ clustering Techniques”, in IEEE, 2012.

19. Juntao Wang and Xiaolong Su, \"An improved k-mean clustering algorithm\", in IEEE, pp 44-46, 2011.

20. Hazem M. El-bakry, “Human Iris Detection Using Fast Cooperative Modular Neural Nets,” Proc. of INNSIEEE International Joint Conference on Neural Networks, pp. 577-582, 14-19 July, 2001, Washington, DC, USA.

21. D. J. Hurley, M. S. Nixon, and J. N. Carter, “Automatic ear recognition by force field transformations,” in Visual Biometrics(Ref. No. 2000/018), IEE Colloquium on, pp. 7/1–7/5, IET, 2000.

22. P. Yan and K. Bowyer, “Empirical evaluation of advanced ear biometrics,” in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)- Workshops - Volume 03 CVPR ’05, pp. 41–41, IEEE, 2005.

23. S. Ansari and P. Gupta, “Localization of ear using outer helix curve of the ear,” in Computing: Theory and Applications, 2007. ICCTA’07. International Conference on, pp. 688–692, IEEE, 2007.

24. L. Yuan and Z.-C. Mu, “Ear detection based on skin-color and contour information,” in Machine Learning and Cybernetics, 2007 International Conference on, vol. 4, pp. 2213–2217, IEEE, 2007.

25. S. Prakash, U. Jayaraman, and P. Gupta, “A skin-color and template based technique for automatic ear detection,” in Advances in Pattern Recognition, 2009. ICAPR’09. Seventh International Conference on, pp. 213–216, IEEE, 2009.

26. H. Cummings, M. S. Nixon, and J. N. Carter, “A novel ray analogy for enrolment of ear biometrics,” in Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on, pp. 1–6, IEEE, 2010.

27. D. J. Hurley, M. S. Nixon, and J. N. Carter, “Force field feature extraction for ear biometrics,” Computer Vision and Image Understanding, vol. 98, no. 3, pp. 491–512, 2005.

28. M. Burge and W. Burger, “Ear biometrics for machine vision,” 1997.

29. M. Choras, “Ear biometrics based on geometrical method of feature extraction,” in ´ Articulated Motion and Deformable Objects, pp. 51–61, Springer volume 3179 of Lecture Notes in Computer Science, 2004.

30. D. Shailaja and P. Gupta, “A simple geometric approach for ear recognition,” in Information Technology, 2006. ICIT’06. 9th International Conference on, pp. 164–167, IEEE, 2006.

31. P. Yan and K. W. Bowyer, “Biometric recognition using 3D ear shape,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, no. 8, pp. 1297–1308, 2007.

32. S. Prakash and P. Gupta, “An efficient technique for ear detection in 3D: Invariant to rotation and scale,” in Biometrics (ICB), 2012 5th IAPR International Conference on, pp. 97–102, IEEE, 2012.

33. J. Zhou, S. Cadavid, and M. Abdel-Mottaleb, “Histograms of categorized shapes for 3D ear detection,” in Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on, pp. 1–6, IEEE, 2010.

34. S. M. Islam, R. Davies, M. Bennamoun, and A. S. Mian, “Efficient detection and recognition of 3D ears,” International Journal of Computer Vision, vol. 95, no. 1, pp. 52–73, 2011.

35. H. Chen and B. Bhanu, “Human ear recognition in 3D,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, no. 4, pp. 718–737, 2007.

36. S. Islam, M. Bennamoun, A. Mian, and R. Davies, “A fully automatic approach for human recognition from profile images using 2D and 3D ear data,” in Proc. of the 4th Intl Symposium on 3DPVT, pp. 131–141, 2008.

37. T. Theoharis, G. Passalis, G. Toderici, and I. A. Kakadiaris, “Unified 3D face and ear recognition using wavelets on geometry images,” Pattern Recognition, vol. 41, no. 3, pp. 796–804, 2008.

38. S. M. Islam, M. Bennamoun, A. S. Mian, and R. Davies, “Score level fusion of ear and face local 3D features for fast and expression-invariant human recognition,” in Image Analysis and Recognition, pp. 387–396, Springer volume 5627 of Lecture Notes in Computer Science, 2009.

39. J. D. Bustard and M. S. Nixon, “3D morphable model construction for robust ear and face recognition,” in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 2582–2589, IEEE, 2010.

40. http://www.tutorialspoint.com/biometrics/biometrics_overview.htm

41. http://bio-metrica.com/biometric-theory

42. https://sarwiki.informatik.hu-berlin.de/Biometry

43. http://electronicimaging.spiedigitallibrary.org/article.aspx?articleid=1909992

44. http://www.hindawi.com/journals/jc/2012/635190/fig1

45. http://futurehumanevolution.com/artificial-intelligence-future-human-evolution/artificial-neural Networks.

Keywords

Biometric, Ear Recognition, Neural Network, k-mean clustering, centroid selection method, FAR, FRR, Accuracy, Error rate.