Geometric statistics-based descriptor for 3D ear recognition

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Several feature keypoint detection and description techniques have been proposed in the literature for 3D shape recognition. These techniques work well in discriminating different classes of shapes; however, they fail when used for comparing highly similar objects such as 3D ear or face in biometric applications. In this paper, we propose an efficient feature keypoint detection and description technique using geometric statistics for representation and matching of highly similar 3D objects and demonstrate its effectiveness in 3D ear-based biometric recognition. To compute the descriptor, we first extract feature keypoints from the 3D data by making use of surface variations followed by defining a descriptor vector for each keypoint. The descriptor vector is generated using three components. To compute the first component, concentric spheres that divide the space around a keypoint into annular regions are considered. Points falling in the annular regions are projected onto a plane perpendicular to the oriented normal of the keypoint. Lower-order moments of the 2D histogram of the spatial distribution of these projected points for each annular region are computed and used to define the first component of the descriptor vector. Next, component of the descriptor vector is computed using histograms of the inner products of the normals of the keypoint and its neighbours. The third component of the descriptor vector encodes the signed distances of the neighbours of the keypoint from the projection plane. Before concatenating individual components of the descriptor vector, the values are normalized to a common scale. Experiments on University of Notre Dame public database-Collection J2 (UND-J2) have achieved a rank-1 and rank-2 identification rates of \(98.60\%\) and \(100\%\), respectively, with an equal error rate of \(1.50\%\). Comparative results show the superiority of the proposed descriptor in recognizing highly similar objects like human ear.

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  1. 1.

    Bhanu, B., Chen, H.: Human Ear Recognition by Computer (Advances in Pattern Recognition), 1st edn. Springer, Berlin (2008)

  2. 2.

    Prakash, S., Gupta, P.: Ear Biometrics in 2D and 3D: Localization and Recognition. Springer, Berlin (2015)

  3. 3.

    Yu, F., Lu, Z., Luo, H., Wang, P.: Three-Dimensional Model Analysis and Processing. Springer, New York (2011)

  4. 4.

    Seng Chua, C., Jarvis, R.: Point signatures—a new representation for 3D object recognition. Int. J. Comput. Vis. 25(1), 63–85 (1997)

  5. 5.

    Zhou, W., Ma, C., Yao, T., Chang, P., Zhang, Q., Kuijper, A.: Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes. Vis. Comput. 1–17 (2018).

  6. 6.

    Guo, Y., Sohel, F.A., Bennamoun, M., Lu, M., Wan, J.: Rotational projection statistics for 3D local surface description and object recognition. Int. J. Comput. Vis. 105(1), 63–86 (2013)

  7. 7.

    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)

  8. 8.

    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation (ICRA’09), pp. 1848–1853 (2009)

  9. 9.

    Stein, F., Grard, M.: Structural indexing: efficient 2D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14(12), 1198–1204 (1992)

  10. 10.

    Zhong, Y.: Intrinsic shape signatures: a shape descriptor for 3d object recognition. In: Computer Vision Workshops (ICCV Workshops), pp. 689–696 (2009)

  11. 11.

    Salti, S., Tombari, F., Stefano, L.D.: SHOT—unique signatures of histograms for surface and texture description. Comput. Vis. Image Underst. 125, 251–264 (2014)

  12. 12.

    Abaza, A., Ross, A., Hebert, C., Harrison, M.A.F., Nixon, M.S.: A survey on ear biometrics. ACM Comput. Surv. (CSUR) 45(2), 22 (2013)

  13. 13.

    Pflug, A., Busch, C.: Ear biometrics: a survey of detection, feature extraction and recognition methods. IET Biom. 1(2), 114–129 (2012)

  14. 14.

    Emeršič, Ž., Štruc, V., Peer, P.: Ear recognition: more than a survey. Neurocomputing 255, 26–39 (2017)

  15. 15.

    Chen, H., Bhanu, B.: Efficient recognition of highly similar 3D objects in range images. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 172–179 (2009)

  16. 16.

    Chen, H., Bhanu, B.: 3D free-form object recognition in range images using local surface patches. Pattern Recogn. Lett. 28(10), 1252–1262 (2007)

  17. 17.

    Chen, H., Bhanu, B.: Human ear recognition in 3D. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 718–737 (2007)

  18. 18.

    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

  19. 19.

    Chen, H., Bhanu, B.: Contour matching for 3D ear recognition. In: Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION’05), vol. 1, pp. 123–128 (2005)

  20. 20.

    Jindan, Z., Cadavid, S., Abdel-Mottaleb, M.: A computationally efficient approach to 3d ear recognition employing local and holistic features. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 98–105 (2011)

  21. 21.

    Prakash, S., Gupta, P.: Human recognition using 3D ear images. Neurocomputing 140, 317–325 (2014)

  22. 22.

    Islam, S.M.S., Davies, R., Bennamoun, M.: Efficient detection and recognition of 3D ears. Int. J. Comput. Vis. 95(1), 52–73 (2011)

  23. 23.

    Passalis, G., Kakadiaris, I.A., Theoharis, T., Toderici, G., Papaioannou, T.: Towards fast 3D ear recognition for real-life biometric applications. In: Proceedings of the Advanced Video and Signal Based Surveillance (AVSS), pp. 39–44 (2007)

  24. 24.

    Yan, P., Bowyer, K.W.: Multi-biometrics 2d and 3d ear recognition. In: Proceedings of the Audio and Video Based Biometric Person Authentication (AVBPA), pp. 503–512 (2005)

  25. 25.

    Yan, P., Bowyer, K.W.: An automatic 3D ear recognition system. In: Proceedings of the 3DPVT, pp. 326–333 (2006)

  26. 26.

    Yan, P., Bowyer, K.W.: Biometric recognition using 3D ear shape. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1297–1308 (2007)

  27. 27.

    Cadavid, S., Abdel-Mottaleb, M.: 3-D ear modeling and recognition from video sequences using shape from shading. IEEE Trans. Inf. Forensics Secur. 3(4), 709–718 (2008)

  28. 28.

    Ding, Z., Zhang, L., Li, H.: A novel 3D ear identification approach based on sparse representation. In: Proceedings of the Image Processing (ICIP), pp. 4166–4170 (2013)

  29. 29.

    Zeng, H., Dong, J.Y., Mu, Z.C., Guo, Y.: Ear recognition based on 3D keypoint matching. In: Proceedings of the Signal Processing (ICSP), pp. 1694–1697 (2010)

  30. 30.

    Liu, Y., Zhang, B., Zhang, D.: Ear-parotic face angle: a unique feature for 3d ear recognition. Pattern Recogn. Lett. 53, 9–15 (2015)

  31. 31.

    Sun, X., Wang, G., Wang, L., Sun, H., Wei, X.: 3D ear recognition using local salience and principal manifold. Graph. Models 76(5), 402–412 (2014)

  32. 32.

    Dong, X., Guo, Y.: 3D ear recognition using SIFT keypoint matching. Energy Procedia 11, 1103–1109 (2011)

  33. 33.

    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

  34. 34.

    Sun, X.P., Li, S.H., Han, F., Wei, X.P.: 3D ear shape matching using joint-entropy. J. Comput. Sci. Technol. 30(3), 565–577 (2015)

  35. 35.

    Zhang, Y., Haniza, A.B.: Vertex-based anisotropic smoothing of 3D mesh data. In: Electrical and Computer Engineering, CCECE’06, pp. 202–205 (2006)

  36. 36.

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Correspondence to Iyyakutti Iyappan Ganapathi.

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Author Surya Prakash has received research grants from Science & Engineering Research Board (SERB) and declares no conflict of interest.

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Ganapathi, I.I., Ali, S.S. & Prakash, S. Geometric statistics-based descriptor for 3D ear recognition. Vis Comput 36, 161–173 (2020) doi:10.1007/s00371-018-1593-8

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  • Biometrics
  • 3D ear recognition
  • Shape descriptor
  • Geometric statistics
  • Local features
  • Identification accuracy.