Author : N Sai Ramya 1
Date of Publication :7th March 2016
Abstract: The primary aim of this project is to implement techniques for fingerprint image enhancement and minutiae extraction. Recognition of people by means of their biometric characteristics very popular among the society. But a fingerprint image consists of enormous amount of data. For a given whole fingerprint, divide it into small blocks called patches. Obtaining an over complete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination of dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. Large volume of fingerprint is collected and stored everyday in a wide range of applications. The experiments demonstrate that this is efficient compared with several competing compression techniques especially at high compression ratios. There are many image compression techniques available. Fingerprint images are rarely of perfect quality. There are many image compression techniques available. JPEG, JPEG 2000, Wavelet Scalar Quantization (WSQ) are the existing image compression techniques. The JPEG, JPEG 2000 methods are for general image compression. Fingerprint identification methods are widely used by police agencies and customhouse to identify criminals or transit passengers since the late nineteenth century. ISO standardized the characteristics of the fingerprint in 2004. After the image enhancement construct a base matrix whose columns represent features of the fingerprint images, referring the matrix dictionary whose columns are called atoms.
Reference :
-
- D. Maltoni, D. Miao, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, 2nd ed. London, U.K.: Springer-Verlag, 2009.
- N. Ahmed, T. Natarajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput., vol. C-23, no. 1, pp. 90–93, Jan. 1974.
- C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms: A Primer. Upper Saddle River, NJ, USA: Prentice-Hall, 1998.
- W. Pennebaker and J. Mitchell, JPEG—Still Image Compression Standard.New York, NY, USA: Van Nostrand Reinhold, 1993.
- M. W. Marcellin, M. J. Gormish, A. Bilgin, and M. P. Boliek,“An overview of JPEG-2000,” in Proc. IEEE Data Compress. Conf.,Mar. 2000, pp. 523–541.
- A. Skodras, C. Christopoulos, and T. Ebrahimi, “The JPEG 2000 stillimage compression standard,” IEEE Signal Process. Mag., vol. 11, no. 5,pp. 36–58, Sep. 2001.
- T. Hopper, C. Brislawn, and J. Bradley, “WSQ grayscale fingerprintimage compression specification,” Federal Bureau of Investigation,Criminal Justice Information Services, Washington, DC, USA, Tech.Rep. IAFIS-IC0110-V2, Feb. 1993
- C. M. Brislawn, J. N. Bradley, R. J. Onyshczak, and T. Hopper, “FBI compression standard for digitized fingerprint images,” Proc. SPIE,vol. 2847, pp. 344–355, Aug. 1996.
- A. Said and W. A. Pearlman, “A new, fast, and efficient image codecbased on set partitioning in hierarchical trees,” IEEE Trans. CircuitsSyst. Video Technol., vol. 6, no. 3, pp. 243–250, Jun. 1996.
- R. Sudhakar, R. Karthiga, and S. Jayaraman, “Fingerprint compressionusing contourlet transform with modified SPIHT algorithm,” IJECEIranian J. Electr. Comput. Eng., vol. 5, no. 1, pp. 3–10, 2005.
- S. G. Mallat and Z. Zhang, “Matching pursuits with timefrequencydictionaries,” IEEE Trans. Signal Process., vol. 41, no. 12,pp. 3397–3415, Dec. 1993.
- S. S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM Rev., vol. 43, no. 1, pp. 129–159, 2001.
- J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust facerecognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, Feb. 2009.
- M. Elad and M. Aharon, “Image denoising via sparse and redundant representation over learned dictionaries,” IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736– 3745, Dec. 2006.
- S. Agarwal and D. Roth, “Learning a sparse representation for object detection,” in Proc. Eur. Conf. Comput. Vis., 2002, pp. 113–127.