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Registration of Three Dimensional Human Face Images across Pose and Their Applications in Digital Forensic

  • Parama Bagchi
  • Debotosh Bhattacharjee
  • Mita Nasipuri
  • Dipak Kumar Basu
Part of the Studies in Computational Intelligence book series (SCI, volume 555)

Abstract

In digital forensic, three-dimensional face recognition is very challenging problem. The problem, with two-dimensional face recognition, is that, pose variation, illumination changes and expressions tend to reduce the face recognition rate. The problem aggravates in case of pose variations, especially when, the poses are rendered across extreme variations e.g. across 90 degrees. In contrast to two dimensional images, three dimensional images tend to reduce the shortcomings of two dimensional approaches. Since three dimensional images works with depth information, they do not depend on illumination, thus making the facial recognition system more robust. Pose variation affects three dimensional recognition rate hence, for a face to be recognized; it should be perfectly registered in three dimensional framework. In this book chapter, a comparative analysis of registration methods is presented for face recognition across different poses from 0 to 90°. Also, various registration approaches that are able to generalize identity, illumination and can also handle a given set of poses have been discussed in later sections. Also, several approaches used in the field of three dimensional face registration and recognition and their importance in digital forensics have been discussed. The application area of 3D faces recognition, in digital forensic, lies with the fact that, if the face of a person is oriented across a certain angle, he cannot be recognised in that position. So, perfect registration is very necessary to reconstruct the face, to be correctly recognized especially, for identifying subjects required for forensic study. Nowadays, photographic expert members from crime Investigation Bureau are researching for a number of new technologies such as facial recognition software, 3D modelling for crime scenes etc because most CCTV footages have very low picture quality which may not be much helpful for investigations. 3D face recognition system would be beneficial because they would help one to visualize the entire system, in all possible orientations.

Keywords

Registration RBF SSR Histograms biometric digital forensic 

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References

  1. 1.
    Jain, A.K., Kumar, A.: Biometrics of Next Generation: An Overview. In: Proc. of Second Generation Biometrics. Springer (2010)Google Scholar
  2. 2.
    Wood Jr., J.D., Horn, C., Gtaune, J., Thomas, A.: Biometrics A Look at Facial Recognition. Documented Reading published in (2003)Google Scholar
  3. 3.
    Bowyer, K., Chang, K., Flynn, P.: A survey of multi-modal two-dimensional + three- dimensional face recognition. Technical Report, Notre Dame Department of Computer Science and Engineering (2004)Google Scholar
  4. 4.
    Bagchi, P., Bhattacharjee, D., Nasipuri, M., Basu, D.K.: A novel approach for nose tip detection using smoothing by weighted median filtering applied to three dimensional face images in variant poses. In: Proc. of Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, March 21-23, p. 272. IEEE, Periyar University (2012)Google Scholar
  5. 5.
    Bagchi, P., Bhattacharjee, D., Nasipuri, M., Basu, D.K.: A Novel Approach in detect- ing pose orientation of a three dimensional face required for face registration. In: Proc. of International Conference on Intelligent Infrastructure, Science City, Kolkata, December 1-2, pp. 1–2 (2012)Google Scholar
  6. 6.
    Xu, C., Wang, Y., Tan, T., Quan, L.: Automatic three dimensional face recognition com- bining global geometric features with local shape variation information. In: Proc. AFGR, pp. 308–313 (2004)Google Scholar
  7. 7.
    Zhang, L., Fonseca, M.D., Ferreira, A.: Survey on three dimensional shape descriptors. In: Proceedings of SPIE Conference on Nonlinear Image Processing and Pattern (2007)Google Scholar
  8. 8.
    Lee, Y., Park, K., Shim, J., Yi, T.: Three dimensional face recognition using statistical multiple features for the local depth information. In: Proc. ICVI (2003)Google Scholar
  9. 9.
    Lu, X., Colbry, D., Jain, A.K.: Three dimensional Model Based Face Recognition. In: Proc. ICPR (2004)Google Scholar
  10. 10.
    Akgul, C.B.: Three dimensional Shape Descriptors and Similarity Learning. ThesisGoogle Scholar
  11. 11.
    Saupe, D., Vranic, D.V.: Three dimensional model retrieval with spherical harmonics and moments. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, p. 392. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Vrani, D.V., Saupe, D.: Description of three dimensional-shape using a complex function on the sphere. In: Proc. of the IEEE International Conference on Multimedia and Expo (ICME 2002), Lausanne, Switzerland, pp. 177–180 (August 2002)Google Scholar
  13. 13.
    Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., Jacobs, D.: A search engine for three dimensional models. Proc. of ACM Trans. Graph, 83–105 (2003)Google Scholar
  14. 14.
    Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of three dimensional shape descriptors. In: Proc. of the 2003 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing (SGP 2003), Aire-la-Ville, Switzerland, pp. 156–164. Eurographics Association (2003)Google Scholar
  15. 15.
    Podolak, J., Shilane, P., Golovinskiy, A., Rusinkiewicz, S., Funkhouser, T.: A planar reflective symmetry transform for three dimensional shapes. In: Proc. of ACM SIGGRAPH (2006)Google Scholar
  16. 16.
    Paquet, E., Murching, A., Naveen, T., Tabatabai, A., Rioux, M.: Description of shape information for two-dimensional and three dimensional objects. Proc. of Signal Processing: Image Communication 16, 103–122 (2000)Google Scholar
  17. 17.
    Paquet, E., Rioux, M.: Nefertiti, A query by content software for three dimensional models databases management. In: Proc. of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling (NRC 1997), Washington, DC, USA, p. 345. IEEE Computer Society (1997)Google Scholar
  18. 18.
    Paquet, E., Rioux, M.: Nefertiti, A tool for three dimensional shape databases management. Proc of SAE Transactions: Journal of Aerospace 108, 387–393 (2000)Google Scholar
  19. 19.
    Ankerst, M., Kastenmüller, G., Kriegel, H.-P., Seidl, T.: Three dimensional shape histograms for similarity search and classification in spatial databases. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651, pp. 207–226. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  20. 20.
    Duta Gaci, H., Sankur, B., Yemez, Y.: Transform-based methods for indexing and re- trieval of three dimensional objects. Three Dimensionalim, 188–195 (2005)Google Scholar
  21. 21.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. PAMI 24(4), 509–522 (2002)CrossRefGoogle Scholar
  22. 22.
    Kortgen, M., Park, G.-J., Nonvoting, M., Klein, R.: Three dimensional shape matching with three dimensional shape contexts. In: Proc. of The 7th Central European Seminar on Computer Graphics (April 2003)Google Scholar
  23. 23.
    Horn, B.K.P.: Extended Gaussian images. Proc. of the IEEE 72, 1671–1686 (1984)CrossRefGoogle Scholar
  24. 24.
    Kang, S.B., Ikeuchi, K.: The complex EGI: A new representation for three- dimensional pose determination. Proc. of IEEE Trans. Pattern Anal. and Mach. Intell. 15(7), 707–721 (1993)CrossRefGoogle Scholar
  25. 25.
    Koendering, J.: Solid shape. In: Proc of. The MIT Press (1990)Google Scholar
  26. 26.
    Bagchi, P., Bhattacharjee, D., Nasipuri, M., Basu, D.K.: A method for nose-tip based three dimensional face registration using maximum intensity algorithm. In: Proc of Proceedings IC3A, JIS College of Engineering, January 11-12 (2013)Google Scholar
  27. 27.
    Zaharia, T., Preteux, F.: Shape-based retrieval of three dimensional mesh models. In: Proc. of the IEEE International Conference on Multimedia and Expo (ICME 2002), Lausanne, Switzerland (August 2002)Google Scholar
  28. 28.
    Zaharia, T., Preteux, F.: Three dimensional shape-based retrieval within the MPEG-7 frame work. In: Proceedings SPIE Conference on Nonlinear Image Processing and Pattern Analysis XII, San Jose, CA, vol. 4304, pp. 133–145 (January 25, 2013)Google Scholar
  29. 29.
  30. 30.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Parama Bagchi
    • 1
  • Debotosh Bhattacharjee
    • 2
  • Mita Nasipuri
    • 2
  • Dipak Kumar Basu
    • 2
  1. 1.Dept. of CSEMCKV Institute of EngineeringKolkataIndia
  2. 2.Dept. of CSEJadavpur UniversityKolkataIndia

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