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3D Ear Shape Matching Using Joint α-Entropy

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Abstract

In this article, we investigate the use of joint α-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint α-entropy. First, we extract a sufficient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree (MST) on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint α-entropy — the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint α-entropy is used to deal with 3D ear shape recognition.

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References

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

    Article  Google Scholar 

  2. Burge M, Burger W. Ear biometrics in computer vision. In Proc. the 15th Int. Conf. Pattern Recognition, Sept. 2000, pp.822–826.

  3. Choras M. Ear biometrics based on geometrical method of feature extraction. In Lecture Notes in Computer Science 3179, Perales F J, Draper B A (eds.), Springer Berlin Heidelberg, 2004, pp.51–61.

  4. Hurley D, Nixon M, Carter J. Force field feature extraction for ear biometrics. Computer Vision and Image Understanding, 2005, 98 (3): 491–512.

    Article  Google Scholar 

  5. Chang K, Bowyer K W, Sarkar S, Victor B. Comparison and combination of ear and face images in appearancebased biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9): 1160–1165.

    Article  Google Scholar 

  6. Zhang H, Mu Z, Liu K. Ear recognition method based on independent component analysis. Pattern Recognition and Artificial Intelligence, 2006, 19(5): 685–688. (in Chinese)

  7. Chen H, Bhanu B, Wang R. Performance evaluation and prediction for 3D ear recognition. In Proc. the 5th Int. Conf. Audio- and Video-Based Biometric Person Authentication, July 2005, pp.748-757.

  8. Besl P J, McKay N D. A method for registration of 3D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239–256.

    Article  Google Scholar 

  9. Shiozaki A. Edge extraction using entropy operator. Computer Vision, Graphics, and Image Processing, 1986, 36(1): 1–9.

    Article  Google Scholar 

  10. Ma B, Hero A, Gorman J, Michel O. Image registration with minimum spanning tree algorithm. In Proc. IEEE Int. Conf. Image Processing, Sept. 2000, pp.481–484.

  11. Ben Hamza A, He Y, Krim H. An information divergence measure for ISAR image registration. In Proc. the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, Aug. 2001, pp.130–133.

  12. Bai X, Latecki L. Path similarity skeleton graph matching. IEEE transactions on Pattern Analysis and Machine Intelligence, 2008, 30(7): 1282–1292.

    Article  Google Scholar 

  13. Liu Y, Chen Z, Tang K. Construction of iso-contours, bisectors, and Voronoi diagrams on triangulated surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1502–1517.

    Article  Google Scholar 

  14. Tang J, Jiang B, Luo B, Kong M. Shape description and skeleton graph matching algorithm based on histogram. Journal of South China University of Technology, 2010, 38(7): 27–32. (in Chinese)

  15. Tang J, Jiang B, Luo B. Graph matching based on graph histogram and path similarity. Journal of Computer-Aided Design and Computer Graphics, 2011, 23(9): 1481–1489. (in Chinese)

  16. Mian A, Bennamoun M, Owens R. Key point detection and local feature matching for textured 3D face recognition. International Journal of Computer Vision, 2008, 79(1): 1–12.

    Article  Google Scholar 

  17. Islam S M S, Davies R, Bennamoun M, Mian A. Efficient detection and recognition of 3D ear. International Journal of Computer Vision, 2011, 95(1): 52–73.

    Article  Google Scholar 

  18. Sun X, Wang X, Wang G, Han F, Wang L. 3D ear shape feature optimal matching using bipartite graph. In Lecture Notes in Electrical Engineering 269, Li S, Jin Q, Jiang X, James J, Park J(eds.), Springer, 2013, pp.3133–3138.

  19. Lu Z, Chen W. Medical image registration using cooccurrence mutual in formation. Chinese Journal of Computers, 2007, 30(6): 1022–1027. (in Chinese)

  20. Sabuncu M, Ramadge P. Using spanning graphs for efficient image registration. IEEE Transactions on Image Processing, 2008, 17(5): 788–797.

    Article  MathSciNet  Google Scholar 

  21. Zhi L, Zhang S, Zhao D, Yu H, Zhao H, Lin S. DoG key points medical image registration based on minimum spanning tree. Journal of Image and Graphics, 2011, 16(4): 647–653. (in Chinese)

  22. Escolano F, Hancock E, Lozano M. Graph matching through entropic manifold alignment. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, Jun. 2011, pp.2417–2424.

  23. Liu Y, Zheng Y, Lv L, Xuan Y, Fu X. 3D model retrieval based on color + geometry signatures. The Visual Computer, 2012, 28(1): 75–86.

    Article  Google Scholar 

  24. Huang Q, Zhang G, Gao L, Hu S, Butscher A, Guibas L. An optimization approach for extracting and encoding consistent maps in a shape collection. ACM Transactions on Graphics, 2012, 31(6): 167:1–167:11.

  25. Cheng Z, Chen Y, Martin R, Lai Y, Wang A. SuperMatching: Feature matching using supersymmetric geometric constraints. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(11): 1885–1894.

    Article  Google Scholar 

  26. Enkhbayar A, Katsutsugu M, Kouichi K. Pairwise matching of 3D fragments using fast Fourier transform. The Visual Computer, 2014, 30(6/7/8): 929–938.

  27. Barra V, Biasotti S. 3D shape retrieval and classification using multiple kernel learning on extended Reeb graphs. The Visual Computer, 2014, 30(11): 1247–1259.

    Article  Google Scholar 

  28. Tam G K L, Martin R R, Rosin P L, Lai Y. Diffusion pruning for rapidly and robustly selecting global correspondences using local isometry. ACM Trans. Graph, 2014, 33(1): Article No. 4.

  29. Liu Z, Bu S, Zhou K, Gao S, Han J, Wu J. A survey on partial retrieval of 3D shapes. J. Comput. Sci. Technol., 2013, 28(5): 836–851.

    Article  Google Scholar 

  30. Rényi A. On measures of entropy and information. In Proc. the 4th Berkeley Symposium on Mathematical Statistics and Probability, June 20-July 30, 1960, pp.547–561.

  31. Rocha L M, Falcao A X, Meloni L G P. Data clustering based on optimum-path forest and probability density function. Technical Report IC-07-031, Institute of Computing, State University of Campinas, 2007.

  32. Hero A, Ma B, Michel O et al. Applications of entropic spanning graphs. IEEE Signal Processing Magazine, 2002, 19(5): 85–95.

    Article  Google Scholar 

  33. Redmond C, Yukich J. Asymptotics for Euclidean functionals with power weighted edges. Stochastic Processes and Their Applications, 1996, 61(2): 289–304.

    Article  MATH  MathSciNet  Google Scholar 

  34. Yan P, Bowyer K. Biometric recognition using three dimensional ears shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(8): 1297–1308.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Systems, School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, China

    Xiao-Peng Sun, Si-Hui Li & Feng Han

  2. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xiao-Peng Sun

  3. School of Mechanical and Engineering, Dalian University of Technology, Dalian, 116024, China

    Xiao-Peng Wei

  4. Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Dalian, 116622, China

    Xiao-Peng Wei

Authors
  1. Xiao-Peng Sun
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  2. Si-Hui Li
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  3. Feng Han
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  4. Xiao-Peng Wei
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Correspondence to Xiao-Peng Sun.

Additional information

It was supported in part by the National Natural Science Foundation of China under Grant Nos. 61472170, 61170143, 60873110, and Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia under Grant No. ITSM201301.

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Sun, XP., Li, SH., Han, F. et al. 3D Ear Shape Matching Using Joint α-Entropy. J. Comput. Sci. Technol. 30, 565–577 (2015). https://doi.org/10.1007/s11390-015-1546-x

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  • DOI: https://doi.org/10.1007/s11390-015-1546-x

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