Fingerprint Compression Using Contourlet Transform and Multistage Vector Quantization

This paper presents a new fingerprint coding technique based on contourlet transform and multistage vector quantization. Wavelets have shown their ability in representing natural images that contain smooth areas separated with edges. However, wavelets cannot efficiently take advantage of the fact that the edges usually found in fingerprints are smooth curves. This issue is addressed by directional transforms, known as contourlets, which have the property of preserving edges. The contourlet transform is a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. The computation and storage requirements are the major difficulty in implementing a vector quantizer. In the full-search algorithm, the computation and storage complexity is an exponential function of the number of bits used in quantizing each frame of spectral information. The storage requirement in multistage vector quantization is less when compared to full search vector quantization. The coefficients of contourlet transform are quantized by multistage vector quantization. The quantized coefficients are encoded by Huffman coding. The results obtained are tabulated and compared with the existing wavelet based ones.

Authors:

• S. Esakkirajan
• T. Veerakumar
• V. Senthil Murugan
• R. Sudhakar

Keywords:

• Contourlet Transform
• Directional Filter bank
• Laplacian Pyramid
• Multistage Vector Quantization

References:

[1] Pennebaker W.B. and Mitchell J.L, JPEG-Still Image Data Compression
Standards. Van Nostrand Reinhold, 1993.
[2] C.M. Brislawn, J.N. Bradley and R.J. Onyschczak and T. Hopper, "The
FBI Compression Standard for Digitized Fingerprint Images," in 1996
Proc. SPIE, vol.2847, pp. 344-355.
[3] M.Antonini, M.Barlaud, P. Mathieu, and I.Daubechies, "Image coding
using wavelet transform," IEEE Trans. Image Proc, pp.205-220,
Apr.1992.
[4] M. N. Do and M. Vetterli, "The contourlet transform: an efficient
directional multiresolution image representation," IEEE Trans. Of Image
Processing, vol.14, no.12, pp. 2091-2106, Dec. 2004.
[5] B.H.Juang and A.H.Gray, "Multiple stage vector quantization for speech
coding," in 1982 Proc. IEEE Int.Conf.Acoust, Speech, Signal Processing
(Paris, France), pp.597-600.
[6] K.P. Soman and K.I. Ramachandran, Insight into Wavelets from Theory
to Practice, Prentice Hall India, New Delhi, 2002, ch.9.
[7] A.Gersho and R.M. Gray, Vector Quantization and Signal Compression.
Boston, MA: Kluwer, 1992.
[8] M. N. Do and M.Vetterli, "Pyramidal directional filter banks and
curvelets," in 2001 Proc. Of IEEE Int. Conf. on Image Proc, vol.3,
pp.158-161, Thessaloniki, Greece.
[9] D.D. Y. Po and M. N. Do, "Directional multiscale modeling of images
using the contourlet transform," IEEE Trans. on Image Processing, to
appear, Jun. 2006.
[10] P. J. Burt and E. H. Adelson, "The Laplacian pyramid as a compact
image code," IEEE Trans. on Commun. vol. 31, no. 4, pp. 532-540,
1983.
[11] M. N. Do, "Directional Multiresolution Image Representations,"
Ph.D.Thesis, EPFL, Lausanne, Switzerland, Dec. 2001.
[12] R. H. Bamberger and M. J. T. Smith, "A filter bank for the Directional
decomposition of images: theory and design," IEEE Trans. on Signal
Processing, vol. 40, no. 4, pp. 882-893, Apr. 1992.
[13] Jayshree Karlekar, P.G. Poonacha and U.B. Desai, "Image Compression
using Zerotree and Multistage Vector Quantization", ICIP, Vol.2,
pp.610, 1997.
[14] Hosam Khalil, Kenneth Rose, "Multistage vector quantizer optimization
for packet networks," IEEE Trans. Signal Proc. Vol. 51, No.7, pp.1870-
1879, July 2003.
[15] Y. Linde, A. Buzo and R.M.Gray, "An algorithm for vector quantizer
design," IEEE Trans. Commun. Vol.28, pp.84-95, Jan.1980.
[16] R. Sudhakar, R. Karthiga and S. Jayaraman, "Fingerprint compression
using Contourlet Transform with Modified SPIHT algorithm", IJECE,
vol.5, No.1, pp.3-10, Winter-Spring 2006.
[17] www.biometrics.cse.msu.edu/fingerprint.html.