Contourlet and Fourier Transform Features Based 3D Face Recognition System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 384)


Human face recognition based on geometrical structure has been an area of interest among researchers for the past few decades especially in pattern recognition. 3D Face recognition systems are of interest in this context. The main advantage of 3D Face recognition is the availability of geometrical information of the face structure which is more or less unique for a subject. This paper focuses on the problems of person identification using 3D Face data. Use of unregistered 3D Face data for feature extraction significantly increases the operational speed of the system with huge database enrollment. In this work, unregistered Face data, i.e. both texture and depth is fed to a classifier in spectral representations of the same data. 2-D Discrete Contourlet Transform and 2-D Discrete Fourier Transform is used here for the spectral representation which forms the feature matrix. Fusion of texture and depth statistical information of face is proposed in this paper since the individual schemes are of lower performance. Application of statistical method seems to degrade the performance of the system when applied to texture data and was effective in the case of depth data. Fusion of the matching scores proves that the recognition accuracy can be improved significantly by fusion of scores of multiple representations. FRAV3D database is used for testing the algorithm.


Point cloud Rotation invariance Pose correction Depth map Contourlet transform Fourier transform Spectral transformations CDF Texture map and principal component analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Expression-invariant 3D face recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 62–70. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Beumier, C.: 3D face recognition. In: IEEE Int. Conf. on Computational Intelligence for Homeland Security and Personal Safety (CIHSPS2004), Venice, Italy, July 21-22, 2004Google Scholar
  4. 4.
    Pan, G., Han, S., Wu, Z., Wang, Y.: 3D face recognition using mapped depth images. In: Proceedings of the 2005 IEEEComputer Society Conference on CVPR (CVPR 2005) Workshops, vol. 03, p. 175 (2005)Google Scholar
  5. 5.
    Yuan, X., Lu, J., Yahagi, T.: A Method of 3D face recognition based on principal component analysis algorithm. In: IEEE International Symposium on Circuits and Systems, May 3-26, vol. 4, pp. 3211−3214 (2005)Google Scholar
  6. 6.
    Russ, T., Boehnen, C., Peters, T.: 3D Face recognition using 3D alignment for PCA. In: Proceedings of the 2006 IEEE Computer Society Conference on CVPR (CVPR 2006), vol. 2, pp. 1391−1398 (2006)Google Scholar
  7. 7.
    Mian, A., Bennamoun, M., Owens, R.: Automatic 3D face detection, normalization and recognition. In: Proceedings of the Third International Symposium on 3DPVT (3DPVT 2006), pp. 735−742, June 2006Google Scholar
  8. 8.
    Smirg, O., Mikulka, J., Faundez-Zanuy, M., Grassi, M., Mekyska, J.: Gender recognition using PCA and DCT of face images. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part II. LNCS, vol. 6692, pp. 220–227. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Gao, H., Ekenel, H.K., Stiefelhagen, R.: Pose normalization for local appearance-based face recognition. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 32–41. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Taghizadegan, Y., Ghassemian, H., Naser-Moghaddasi, M.: 3D Face Recognition Method Using 2DPCA-Euclidean Distance Classification. ACEEE International Journal on Control System and Instrumentation 3(1), 5 (2012)Google Scholar
  11. 11.
    Gervei, O., Ayatollahi, A., Gervei, N.: 3D Face Recognition Using Modified PCA Methods. World Academy of Science, Engineering & Technology (39), 264, March 2010Google Scholar
  12. 12.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  13. 13.
    Ramalingam, S.: 3D face recognition: feature extraction based on directional signatures from range data and disparity map. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4397−4402, October 2013Google Scholar
  14. 14.
    Dawi, M., Al-Alaoui, M.A., Baydoun, M.: 3D face recognition using stereo images. In: 17th IEEE Mediterranean Electrotechnical Conference (MELECON), pp. 247−251, April 2014Google Scholar
  15. 15.
    Goswami, G., Vatsa, M., Singh, R.: RGB-D Face Recognition With Texture and Attribute Features. IEEE Transactions on Information Forensics and Security 9(10), 1629–1640 (2014)CrossRefGoogle Scholar
  16. 16.
    Mantecon, T., del-Bianco, C.R., Jaureguizar, F., Garcia, N.: Depth-based face recognition using local quantized patterns adapted for range data. In: IEEE International Conference on Image Processing (ICIP), pp. 293−297 (2014)Google Scholar
  17. 17.
    Mandal, T., Wu, Q.M.J.: 19th International Conference on Pattern Recognition, ICPR 2008, pp.1−4, December 2008Google Scholar
  18. 18.
    Naveen, S., Moni, R.S.: Multimodal Approach for Face Recognition using 3D-2D Face Feature Fusion. International Journal of Image Processing (IJIP) 8(3), 73–86 (2014)Google Scholar
  19. 19.
    Burt, P.J., Adelson, E.H.: The Laplacian Pyramid as a Compact Image Code. IEEE Trans. on Communications, 532−540, April 1983Google Scholar
  20. 20.
    DO, M.N., Vetterli, M.: The contourlet transform: An efficient directional multiresolution image representation. IEEE Trans. Image Process. 14, 2091–2106 (2005)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Jiji, C.V., Chaudhuri, S., Chatterjee, P.: Single Frame Super-resolution: Should we process Locally or Globally. Multidimensional Systems and Signal Processing 18(2–3), 123–152 (2007)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Jiji, C.V., Krishnan, R.: Unni: fusion of multispectral and panchromatic images using nonsubsampled contourlet transform. In: IPCV 2008, pp. 608−613 (2008)Google Scholar
  23. 23.
    Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of ECELBS Institute of Technology for WomenTrivandrumIndia
  2. 2.Department of ECEMarian Engineering CollegeTrivandrumIndia

Personalised recommendations