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3D Face Recognition and Compression

  • Wei Jen ChewEmail author
  • Kah Phooi Seng
  • Wai Chong Chia
  • Li-Minn Ang
  • Li Wern Chew
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 52)

Abstract

Face recognition using 3D images is an important area of research due to its ability to solve problems faced by 2D images like pose changes. In this chapter, a 3D face range recognition and compression system is proposed and the effect of using compressed 3D range images on the recognition rate is investigated. Compression is used to reduce the file size for faster transmission and is performed using the Set Partitioning in Hierarchical Trees (SPIHT) coding method, which is an improvement of the Embedded Zerotree Wavelet (EZW) coding method. Arithmetic Coding (AC) is also performed after SPIHT to further reduce the amount of bits transmitted. Comparing the uncompressed probe images and probe images compressed using SPIHT coding, simulation results show that the compressed image recognition rate ranges from being lower to being slightly higher than uncompressed probe image recognition rate, depending on bit rate. This proves that a 3D face range recognition system using compressed images is a feasible alternative to a system without using compressed images and should be investigated since the benefits like smaller file storage size, faster image transmission time and better recognition rates are important.

Keywords

3D face recognition 3D range image compression SPIHT 

References

  1. 1.
    Identix. (2005). FaceIT surveillance SDK http://www.identix.com/.
  2. 2.
    Zhao, W., Chellappa, R., Rosenfeld, A., & Phillips, P.J. (2000). Face recognition: A literature survey. UMD CfAR Technical Report CAR-TR-948.Google Scholar
  3. 3.
    Saha, S. (2000). Image compression – from DCT to wavelets: A review, http://www.acm.org/crossroads/xrds6--3/sahaimgcoding.html.
  4. 4.
    Said, A., & Pearlman, W.A. (1996). A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 6(3), 243–250.CrossRefGoogle Scholar
  5. 5.
    Rissanen, J.J., & Langdon, G.G., Jr. (Mar 1979). Arithmetic coding (PDF). IBM Journal of Research and Development, 23(2), 149–162.MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Turk M., & Pentland, A. (Mar 1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71–86.CrossRefGoogle Scholar
  7. 7.
    Belhumeur, P.N., Hespanha, J.P., & Kriegman, D.J. (Jul 1997). Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–729.CrossRefGoogle Scholar
  8. 8.
    Chew, W.J., Seng, K.P., Liau, H.F., & Ang, L.-M. (2008). New 3D face matching technique for 3D model based face recognition. Proceeding of International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS2008), pp. 236–239.Google Scholar
  9. 9.
    Besl, P., & McKay, N. (1992). A method for registration of 3-D shapes. IEEE Transaction on Pattern Analysis and Machine Intelligence, 14(2), 239–256.CrossRefGoogle Scholar
  10. 10.
    Flynn, P.J., Bowyer, K.W., & Phillips, P.J. (2003). Assessment of time dependency in face recognition: An initial study. Audio and Video-Based Biometric Person Authentication, Springer-Verlag, New York, USA, 44–51.Google Scholar
  11. 11.
    Chang, K., Bowyer, K.W., & Flynn, P.J. (Dec 2003). Face recognition using 2D and 3D facial data. ACM Workshop on Multimodal User Authentication, pp. 25–32.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Wei Jen Chew
    • 1
    Email author
  • Kah Phooi Seng
    • 1
  • Wai Chong Chia
    • 1
  • Li-Minn Ang
    • 1
  • Li Wern Chew
    • 1
  1. 1.The University of NottinghamSemenyihMalaysia

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