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Multi-model Ear Database for Biometric Applications

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Innovative Approaches and Solutions in Advanced Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 648))

Abstract

We present the 3DEarDB, a multi-model ear database, characterized by different types of ear representation, either 2D or 3D, depending on the acquisition device used. The main objective is to provide the biometrics community with a unified tool for testing and comparing of classification algorithms not only on 2D intensity and/or depth images, or videos, but also on detailed 3D mesh models of human ears. The 3DEarDB features accurate 3D mesh models of right ear captured from more than 100 subjects, with a resolution of 1 mm and an accuracy of 0.05 mm, collected via the VIUscan 3D laser scanner, available at the Smart Lab of IICT-BAS, in the AComIn project frames. Two more ear acquisition modalities are also included: 3D Kinect ear depth maps and 2D high-definition video clips, associated to the basic mesh models. To extend 3DEarDB compatibilities with known methods for 2D/3D ear detection and/or recognition, we provide two more ear model types. Namely, a set of 2D ear intensity projections (of different orientations and/or lightening directions), and a set of 2D depth map projections can be generated by demand from the basic 3D ear models. Finally, we report about preliminary experiments conducted by means of Extended Gaussian Image approach that confirm the consistency of the proposed 3D-Ear-Data-Base.

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Notes

  1. 1.

    http://www.iict.bas.bg/acomin/.

  2. 2.

    http://meshlab.sourceforge.net/.

  3. 3.

    https://www.wolfram.com/mathematica/.

  4. 4.

    https://en.wikipedia.org/wiki/Kinect_for_Xbox_One#Specifications.

  5. 5.

    http://www.olympus-global.com/en/news/2011b/nr111110sh21e.jsp.

References

  1. Day, D.: Biometric applications, overview. In: Li, S.Z., Jain, A.K. (eds.) Encyclopedia of Biometrics, pp. 169–174. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  2. Hurley, D., Nixon, M., Carter, J.: Ear biometrics by force field convergence. In: Proceedings of the 5th International Conference on Audio- Video- Biometric Person Authentication, pp. 386–394 (2005)

    Google Scholar 

  3. Wang, Y., Mu, Z., Zeng, H.: Block-based and multi-resolution methods for ear recognition using wavelet transform and uniform local binary patterns. In: Proceedings of the 19th IEEE International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)

    Google Scholar 

  4. Yan, P., Bowyer, K.: Empirical evaluation of advanced ear biometrics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 41–42. San Diego, CA, USA, ISBN 0-7695-2372-2 (2005)

    Google Scholar 

  5. Bertillon, A.: Signaletic Instructions Including: The Theory and Practice of Anthropometrical Identification (1896)

    Google Scholar 

  6. Iannarelli, A.: Ear Identification, Forensic Identification Series. Paramount Publ. Company, Fremont, CA (1989)

    Google Scholar 

  7. Cummings, A.H., Nixon, M.S., Carter, J.N.: A novel ray analogy for enrolment of ear biometrics share. In: Proceedings of IEEE Fourth Conference on Biometrics: Theory, Applications and Systems, Washington DC, USA, pp. 1–6 (2010)

    Google Scholar 

  8. De Marsico, M., Nappi, M., Riccio, D.: HERO: human ear recognition against occlusions. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 178–183. June 13–18 2010

    Google Scholar 

  9. Chang, K., Bowyer, K.W., Sarkar, S., Victor, B.: Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1160–1165 (2003)

    Article  Google Scholar 

  10. Victor, B., Bowyer, K.W., Sarkar, S.: An evaluation of face and ear biometrics. In: Proceedings of 16th IEEE International Conference on Pattern Recognition (ICPR), pp. 429–432 (2002)

    Google Scholar 

  11. Zhang, H., Mu, Z., Qu, W., L Iu, L., Zhang, C.: A novel approach for ear recognition based on ICA and RBF network. In: Proceedings of the 4th IEEE International Conference on Machine Learning and Cybernetics, pp. 4511–4515 (2005)

    Google Scholar 

  12. Yuan, L., Mu, Z.: Ear recognition based on 2D images. In: First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–5 (2007)

    Google Scholar 

  13. Naseem, I., Togneri, R., Bennamoun, M.: Sparse representation for ear biometrics. In: Proceedings of the 4th International Symposium on Advances in Visual Computing (ISVC), Part II, pp. 336–345 (2008)

    Google Scholar 

  14. Hurley, D., Nixon, M., Carter, J.: Automatic ear recognition by force field transformations. In: Proceedings of the IEEE Colloquium on Visual Biometrics, pp. 7/1–7/5 (2000)

    Google Scholar 

  15. Hurley, D., Nixon, M., Carter, J.: Force field feature extraction for ear biometrics. Comput. Vis. Image Underst. 98(3), 491–512 (2005)

    Article  Google Scholar 

  16. Choras, M., Choras, R.: Geometrical algorithms of ear contour shape representation and feature extraction. In: Proceedings of the 6th IEEE International Conference on Intelligent Systems Design and Applications, pp. 451–456 (2006)

    Google Scholar 

  17. Choras, M.: Ear biometrics based on geometrical feature extraction. Electron. Lett. Comput. Vis. Image Anal. 5(3), 84–95 (2005)

    Google Scholar 

  18. Abate, A., Nappi, M., Riccio, D., Ricciardi, S.: Ear recognition by means of a rotation invariant descriptor. In: Proceedings of the 18th IEEE International Conference on Pattern Recognition (ICPR), pp. 437–440 (2006)

    Google Scholar 

  19. Hailong, Z., Mu, Z.: Combining wavelet transform and orthogonal centroid algorithm for ear recognition. In: Proceedings of the 2nd IEEE International Conference on Computer Science and Information Technology, pp. 228–231 (2009)

    Google Scholar 

  20. Sana, A., Gupta, P.: Ear biometrics: a new approach. In: Proceedings of the 6th International Conference on Advances in Pattern Recognition, 06 Sep. 2006, pp. 1–5 (2007)

    Google Scholar 

  21. Nanni, L., Lumini, A.: A multi-matcher for ear authentication. Pattern Recogn. Lett. 28(16), 2219–2226 (2007)

    Article  MATH  Google Scholar 

  22. Watabe, D., Sai, H., Sakai, K., Andnakamura, O.: Ear biometrics using jet space similarity. In: Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, pp. 1259–1264. Niagara Falls, ON. e-ISBN 978-1-4244-1643-1, May 4–7 2008

    Google Scholar 

  23. Dewi, K., Yahagi, T.: Ear photo recognition using scale invariant keypoints. In: Proceedings of the International Computational Intelligence Conference, pp. 253–258 (2006)

    Google Scholar 

  24. Kisku, D.R., Mehrotra, H., Gupta, P., Sing, J.K.: SIFT-based ear recognition by fusion of detected key-points from color similarity slice regions. In: Proceedings of the IEEE International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), pp. 380–385 (2009)

    Google Scholar 

  25. Chen, H., Bhanu, B.: Human ear detection from side face range images. In: Proceedings of the IEEE International Conference on Pattern Recognition (ICPR), pp. 574–577 (2004)

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Cantoni, V., Dimov, D.T., Nikolov, A.: 3D ear analysis by an EGI representation. In: Cantoni, V., Dimov, D.T., Tistarelli, M. (eds.) Proceedings of the 1st International Workshop on Biometrics, BIOMET June 23–24, 2014, Sofia, Bulgaria. Biometric Authentication, LNCS, vol. 8897, pp. 136–150. Springer, Heidelberg (2014)

    Google Scholar 

  30. Dimov, D.T., Cantoni, V.: Appearance-based 3D object approach to human ears recognition. In: Cantoni, V., Dimov, D.T., Tistarelli, M. (eds.) Proceedings of the 1st International Workshop on Biometrics, BIOMET June 23–24, 2014, Sofia, Bulgaria. Biometric Authentication, LNCS, vol. 8897, pp. 121–135. Springer, Heidelberg (2014)

    Google Scholar 

  31. Barra, S., De Marsico, M., Nappi, M., Riccio, D.: Unconstrained Ear processing: what is possible and what must be done. In: Scharcanski, J., Proença, H., Du, E. (eds.) Signal and Image Proceeding for Biometrics, LNEE, vol. 292, pp. 129–190. Springer, Berlin (2014)

    Chapter  Google Scholar 

  32. Pflug, A.: Ear recognition: biometric identification using 2- and 3-dimensional images of human ears. ISBN: 978-82-8340-007-6, Ph.D. thesis, 205p., Gjøvik Univ. College, 2-2015

    Google Scholar 

  33. Prakash, S., Gupta, P.: Ear biometrics in 2D and 3D—localization and recognition. In: Hammoud, R.I., Wolff, L.B. (eds.) Augm. Vision & Reality, vol. 10. Springer, Singapore (2015)

    Google Scholar 

  34. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)

    Article  Google Scholar 

  35. Gao, W., Cao, B., Shan, S., Zhou, D., Zhang, X., Zhao, D.: CAS-PEAL database (2004). http://www.jdl.ac.cn/peal/

  36. UMIST database (1998). http://www.shef.ac.uk/eee/research/iel/research/face.html

  37. MID. NIST mugshot identification database (1994). http://www.nist.gov/srd/nistsd18.cfm

  38. XM2VTSDB database (1999). http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/

  39. AMI Ear Database. http://www.ctim.es/research_works/ami_ear_database/

  40. Raposo, R., Hoyle, E., Peixinho, A., Proença, H.: UBEAR: a dataset of ear images captured on-the-move in uncontrolled conditions. In: IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 84–89. Paris, France (2011)

    Google Scholar 

  41. UND Databases. http://www.cse.nd.edu/~cvrl/CVRL/Data_Sets.html

  42. USTB Databases. http://www1.ustb.edu.cn/resb/en/index.htm

  43. OpenHear Database. http://www2.imm.dtu.dk/projects/OpenHear/

  44. SYMARE Database. http://www.ee.usyd.edu.au/carlab/symare.htm

  45. HANDY SCAN 3D: The portable 3D scanners for industrial application. http://www.creaform3d.com/sites/default/files/assets/brochures/files/handyscan/Handyscan3D_Brochure_EN_HQ_22052012.pdf

  46. Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: MeshLab: an open-source mesh processing tool. In: Proceedings of Eurographics Italian Chapter Conference, pp. 129–136 (2008)

    Google Scholar 

  47. Vollmer, J., Mencl, R., Müller, H.: Improved laplacian smoothing of noisy surface meshes. Int. Conf. Eurographics 18(3), 131–138 (1999)

    Google Scholar 

  48. Boyé, S., Guennebaud, G., Schlick, C.: Least squares subdivision surfaces. Comput. Graph. Forum. 29(7), 2021–2028 (2010)

    Article  Google Scholar 

  49. Horn, B.K.P.: Extended Gaussian images. Proc. IEEE. 72, 1671–1686 (1984)

    Article  Google Scholar 

  50. Kang, S.B., Horn, B.K.P.: Extended gaussian image (EGI). In: Ikeuchi, K. (ed.) Computer Vision—A Reference Guide, pp. 275–278. Springer, New York (2014)

    Google Scholar 

  51. Bray, J.R., Curtis, J.T.: An ordination of upland forest communities of southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957)

    Article  Google Scholar 

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Acknowledgments

This research is partly supported by the project AComIn “Advanced Computing for Innovation”, grant 316087, funded by the FP7 Capacity Programme “Research Potential of Convergence Regions”.

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Authors and Affiliations

  1. Institute of Information and Communication Technologies (IICT), Bulgarian Academy of Sciences (BAS), Sofia, Bulgaria

    Atanas Nikolov & Dimo Dimov

  2. Department of Industrial and Information Engineering, Pavia University, Pavia, Italy

    Virginio Cantoni

  3. Department of Computer Science, Salerno University, Salerno, Italy

    Andrea Abate & Stefano Ricciardi

Authors
  1. Atanas Nikolov
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  2. Virginio Cantoni
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  3. Dimo Dimov
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  4. Andrea Abate
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  5. Stefano Ricciardi
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Corresponding author

Correspondence to Dimo Dimov .

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Editors and Affiliations

  1. Inst. of Info. & comm. Tech., Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetozar Margenov

  2. INst. of Info. & Comm. Tech., Bulgarian Academy of Sciences, Sofia, Bulgaria

    Galia Angelova

  3. Inst. of Info. & Comm. Tech., Bulgarian Academy of Sciences, Sofia, Bulgaria

    Gennady Agre

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Nikolov, A., Cantoni, V., Dimov, D., Abate, A., Ricciardi, S. (2016). Multi-model Ear Database for Biometric Applications. In: Margenov, S., Angelova, G., Agre, G. (eds) Innovative Approaches and Solutions in Advanced Intelligent Systems . Studies in Computational Intelligence, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-32207-0_11

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  • DOI: https://doi.org/10.1007/978-3-319-32207-0_11

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