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Ear Recognition in 3D

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Ear Biometrics in 2D and 3D

Part of the book series: Augmented Vision and Reality ((Augment Vis Real,volume 10))

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Abstract

This chapter presents an ear recognition technique which makes use of 3D along with co-registered 2D ear images. It presents a two-step matching technique to compare two 3D ears. In the first step, it computes salient 3D data points from 3D ear images with the help of local 2D feature points of co-registered 2D ear images. Subsequently, it uses these salient 3D points to coarsely align 3D ear images. In the second step, it performs final matching of coarsely aligned 3D ear images by using a Generalized Procrustes Analysis (GPA) and Iterative Closest Point (ICP) based matching technique (GPA-ICP). The presented technique has been tested on University of Notre Dame database-Collection J2 (UND-J2) which consists of co-registered 2D and 3D ear images with scale and pose variations. It has achieved a verification accuracy of \(98.30\,\%\) with an equal error rate of \(1.8\,\%\).

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References

  1. Hui, Chen, and Bir, Bhanu. 2005. Contour matching for 3D ear recognition. In Proceedings of IEEE Workshop on Application of Computer Vision (WACV/MOTION’05), vol. 1, 123–128.

    Google Scholar 

  2. Yan, Ping, and Kevin W. Bowyer. 2005. Ear biometrics using 2D and 3D images. In Proceedings of International Conference on Computer Vision and Pattern Recognition-Workshop, 121–128.

    Google Scholar 

  3. Yan, Ping, and Kevin W. Bowyer. 2005. Multi-biometrics 2D and 3D ear recognition. In Proceedings of International Conference on Audio-and Video-Based Biometric Person Authentication. LNCS, vol. 3546, 503–512.

    Google Scholar 

  4. Chen, H., B. Bhanu, and R. Wang. 2005. Performance evaluation and prediction for 3D ear recognition. In Proceedings of International Conference on Audio and Video Based Biometric Person Authentication (AVBPA’05). LNCS, vol. 3546, 748.

    Google Scholar 

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

    Article  Google Scholar 

  6. Chen, Hui, and Bhanu, Bir. 2007. Human ear recognition in 3D. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4): 718–737.

    Google Scholar 

  7. Passalis, G., I.A. Kakadiaris, T. Theoharis, G. Toderici, and T. Papaioannou. 2007. Towards fast 3D ear recognition for real-life biometric applications. In Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS’07), vol. 3, 39–44.

    Google Scholar 

  8. Kirkpatrick, S., C.D. Gelatt Jr. and M.P. Vecchi. 1983. Optimization by simulated annealing. Science 220(4598): 671–680.

    Google Scholar 

  9. Cadavid, S., and M. Abdel-Mottaleb. 2007. Human identification based on 3D ear models. In Proceedings of International Conference on Biometrics: Theory, Applications and Systems (BTAS’07), 1–6.

    Google Scholar 

  10. Islam, S.M.S., M. Bennamoun, A.S. Mian, and R. Davies. 2008. A fully automatic approach for human recognition from profile images using 2D and 3D ear data. In Proceedings of 4th International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT’08), 131–135.

    Google Scholar 

  11. Islam, S.M., R. Davies, A.S. Mian, and M. Bennamoun. 2008. A fast and fully automatic ear recognition approach based on 3D local surface features. In Proceedings of 10th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS’08), 1081–1092.

    Google Scholar 

  12. Islam, S.M.S., Rowan Davies, Mohammed Bennamoun, and Ajmal S. Mian. 2011. Efficient detection and recognition of 3D ears. International Journal of Computer Vision 95(1): 52–73.

    Google Scholar 

  13. Theoharis, Theoharis, Georgios Passalis, George Toderici, and Ioannis A. Kakadiaris. 2008. Unified 3D face and ear recognition using wavelets on geometry images. Pattern Recognition 41(3): 796–804.

    Article  MATH  Google Scholar 

  14. Islam, S.M.S., Mohammed Bennamoun, Ajmal S. Mian, and R. Davies. 2009. Score level fusion of ear and face local 3D features for fast and expression-invariant human recognition. In Proceedings of 6th International Conference on Image Analysis and Recognition (ICIAR’09), 387–396.

    Google Scholar 

  15. John, Bustard, and Mark, Nixon. 2010. 3D morphable model construction for robust ear and face recognition. In Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR’10), 2582–2589.

    Google Scholar 

  16. Jindan, Zhou, S. Cadavid, and M. Abdel-Mottaleb. 2011. A computationally efficient approach to 3D ear recognition employing local and holistic features. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’11), 98–105.

    Google Scholar 

  17. Gower, J.C. 1975. Generalized procrustes analysis. Psychometrika 40(1): 33–51.

    Article  MATH  MathSciNet  Google Scholar 

  18. Schonemann, P. 1966. A generalized solution of the orthogonal procrustes problem. Psychometrika 31(1): 110.

    Article  Google Scholar 

  19. Schonemann, P., and R. Carroll. 1970. Fitting one matrix to another under choice of a central dilation and a rigid motion. Psychometrika 35(2): 245–255.

    Article  Google Scholar 

  20. Borg, Ingwer, and Patrick Groenen. 2005. Modern multidimensional scaling: Theory and applications. New York: Springer.

    Google Scholar 

  21. Commandeur, J.J.F. 1991. Matching configurations. Leiden University, Leiden, Netherlands: DSWO Press.

    Google Scholar 

  22. Roberto, Toldo, Alberto, Beinat, and Fabio, Crosilla. 2010. Global registration of multiple point clouds embedding the generalized procrustes analysis into an ICP framework. InProceedings of 5th International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT’10).

    Google Scholar 

  23. Crosilla, F., and A. Beinat. 2002. Use of generalised procrustes analysis for the photogrammetric block adjustment by independent models. ISPRS Journal of Photogrammetry and Remote Sensing 56(3): 195–209.

    Article  Google Scholar 

  24. Herbert, Bay, Ess Andreas, Tuytelaars Tinne, and Van Gool Luc. 2008. Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3): 346–359.

    Article  Google Scholar 

  25. Bustard, J.D., and M.S. Nixon. 2008. Robust 2D ear registration and recognition based on SIFT point matching. In Proceedings of International Conference on Biometrics: Theory, Applications and Systems (BTAS’08), 1–6.

    Google Scholar 

  26. Lowe, David G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2): 91–110.

    Article  Google Scholar 

  27. Prakash, Surya, and Phalguni Gupta. 2012. An efficient ear localization technique. Image and Vision Computing 30(1): 38–50.

    Article  Google Scholar 

  28. Surya, Prakash, and Phalguni, Gupta. 2012. An efficient technique for ear detection in 3D: Invariant to rotation and scale. In Proceedings of IAPR/IEEE International Conference on Biometrics (ICB’12), 97–102.

    Google Scholar 

  29. Yan, Ping, and Kevin W. Bowyer. 2005. Empirical evaluation of advanced ear biometrics. In Proceedings of International Conference on Computer Vision and Pattern Recognition-Workshop, vol. 3, 41–48.

    Google Scholar 

  30. Kass, M., A. Witkin, and D. Terzopoulos. 1988. Snakes: active contour models. International Journal of Computer Vision 1(4): 321–331.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, India

    Surya Prakash

  2. Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

Authors
  1. Surya Prakash
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  2. Phalguni Gupta
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Correspondence to Surya Prakash .

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© 2015 Springer Science+Business Media Singapore

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Prakash, S., Gupta, P. (2015). Ear Recognition in 3D. In: Ear Biometrics in 2D and 3D. Augmented Vision and Reality, vol 10. Springer, Singapore. https://doi.org/10.1007/978-981-287-375-0_5

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  • DOI: https://doi.org/10.1007/978-981-287-375-0_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-374-3

  • Online ISBN: 978-981-287-375-0

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