Abstract
Wavelet transform combined with the set partitioning coders (SPC) are the most widely used fingerprint image compression approach. Many different SPC coders have been proposed in the literature to encode the wavelet transform coefficients a common feature of which is trying to maximize the global peak-signal-to-noise ratio (PSNR) at a given bit rate. Unfortunately, they have not considered the local variations of SNR within the compressed fingerprint image; therefore, different regions in the compressed image will have different ridge-valley qualities. This problem causes the verification performance to be decreased because minutiae and other useful features cannot be extracted precisely from the low-bit-rate-compressed fingerprint images. Contrast variation within the original image worsens the problem. This paper deals with those applications of fingerprint image compression in which high compression ratios and preserving or improving the verification performance of the compressed images are the main concern. We propose a compression scheme in which the local-SNR (signal-to-noise ratio) variations within the compressed image are minimized (and thus, general quality is maximized everywhere) by means of an iterative procedure. The proposed procedure can be utilized in conjunction with any SPC coder without the need to modify the SPC coder’s algorithm. In addition, we used image enhancement to further improve the ridge-valley quality as well as the verification performance of the compressed fingerprint images through alleviating the leakage effect. We evaluated the compression and verification performances of some conventional and modern SPC coders including STW, EZW, SPIHT, WDR, and ASWDR combined with the proposed scheme. This evaluation was performed on the FVC2004 dataset with respect to measures including average PSNR curve versus bit rate, verification accuracy, detection error trade-off (DET) curve, and correlation of matching scores versus the average quality of involved fingerprint images. Simulation results showed considerable improvement on verification performance of all examined SPC coders, especially the SPIHT coder, by using the proposed scheme.
Similar content being viewed by others
References
Al- Asmari AK (2002) Progressive fingerprint images compression using edge detection technique. Int J Imag Syst Technol 12(5):211–216
Anurakphanawan N, Lamsrichan P (2015) “Fingerprint recognition performance with WSQ, CAWDR, and JPEG2000 compression”. 6th Int Conf Inform Commun Technol Embedded Syst (IC-ICTES) 1–6
Beleznai C, Ramoser H, Wachmann B, Birchbauer J, Bischof H, Kropatsch W (2001) Memory-efficient fingerprint verification. Proc IEEE Int Conf Image Process (ICIP’01) 2:463–466, Thessaloniki, Greece
Bradley JN, Brislawn CM, Hopper T (1993) “The FBI Wavelet/Scalar Quantization standard for gray-scale fingerprint image compression”. SPIE Proc, Visual Inform Process II 293–304, (Orlando, FL, USA)
Cappelli R, Maio D, Maltoni D (2002) “Synthetic fingerprint-database generation”. Proc 16th ICPR
Chen Y, Dass S, Jain A (2005) “Fingerprint quality indices for predicting authentication performance,”. Proc AVBPA 160–170
Chen T, Jiang X, Yau W (2004) “Fingerprint image quality analysis,”. Proc Int Conf Image Process 1253–1256
Chong MMS, Gay RKL, Tan HN, Liu J (1992) Automatic representation of fingerprints for data compression by B-spline functions. Pattern Recogn 25(10):1199–1210
Dhawan S (2011) A review of image compression and comparison of its algorithms. Int J Electron Commun Technol 2(1):22–26
Esakkirajan S, Veerakumar T, Murugan VS, Sudhakar R (2006) Fingerprint compression using contourlet transform and multistage vector quantization. Int J Biol Med Sci 1(2):140–147
Fernandez FA, Fierrez J, Garcia JO, Rodriguez JG, Franthaler H, Kollreider K, Bigun J (2007) A comparative study of fingerprint image-quality estimation methods. IEEE Trans Inform Forensics Sec 2(4):734–743
Gonzales RC, Woods RE (2007) Digital image processing, 3’rd Edition, Prentice Hall
Gonzalo AR (1999) “Fingerprint image compression using wavelets: a comparison with JPEG”. Proc IASTED Int Conf, Sign Image Process
Gornale SS, Humbe VT, Manza RR, Kale KV (2008) Fingerprint image compression using Retain Energy (RE) and Number of Zeros (NZ) through Wavelet Packet (WP). Int J Comput Sci Secur 1(2):35–42
Gornale SS, Manza RR, Humbe V, Kale KV (2007) Performance analysis of biorthogonal wavelet filters for lossy fingerprint image compression. Int J Imag Sci Eng 1(1):16–20
Grasemann U, Miikkulainen R (2005) “Effective image compression using evolved wavelets”. GECCO’05: Proc 2005 Conf Genet Evolution Comput 1961–1968, ACM Press, (New York, NY, USA)
http://www.mathworks.com, Image Processing Toolbox, see documentation for the “imadjust” function
http://www.mathworks.com, Image Processing Toolbox, see documentation for the “imsharpen” function
Islam MR, Bulbul F, Shanta SS (2012) Performance analysis of coiflet-type wavelets for a finger image compression by using wavelet and wavelet packet transform. Int J Comput Sci Eng Surv 3(2):79–87
Jain AK, Ross AA, Nandakumar K (2011) Introduction to biometrics, Springer Sicence+ Business Media, LLC
Kambli MM, Bhatia MS (2010) Comparison of different fingerprint compression techniques”. Sign Image Process: Int J 1(1):27–39
Kampfer M, Stogner A, Uhl H (2007) “Comparison of compression algorithms’ impact on fingerprint and face recognition accuracy”. Proc SPIE, San Jose, CA 6508:650810.1–650810.12
Kasaei S, Deriche M, Boashash D (2002) A novel fingerprint image compression technique using wavelet packets and pyramid lattice vector quantization. IEEE Trans Image Process 12(11):1365–1378
Kidd R (1995) Comparison of wavelet scalar quantization and JPEG for fingerprint image compression. J Electron Imag 4(1):31–39
Lepley MA (2004) Profile for 1000 ppi fingerprint compression, Tech. Rep. MTR 04B0000022, The MITRE Corporation
Lim E, Jiang X, Yau W (2002) “Fingerprint quality and validity analysis,”. Proc Int Conf Image Process 469–472
Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2004) “FVC2004: Third Fingerprint Verification Competition” Springer-Verlag Berlin Heidelberg, D. Zhang and A.K. Jain (Eds.): ICBA 2004, LNCS 3072, 1–7
Maltoni D, Mao D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition, 2nd edn. Springer, London
Pearlman WA, Said A (2008) Set partitioning coding: part I of set partition coding and image wavelet coding systems. Found Trends Sign Process 2(2):95–180
Pearlman WA, Said A (2008) Image wavelet coding systems: part II of set partition coding and image wavelet coding systems. Found Trends Sign Process 2(3):181–246
Pearlman WA, Said A (2011) Digital signal compression: principles and practice. Cambridge University Press, New York
Perumal V, Ramaswamy D (2009) An innovative scheme for effectual fingerprint data compression using bezier curve representations. Int J Comput Sci Inform Sec 6(1):149–157
Said A, Pearlman WA (1993) “Image compression using the spatial-orientation tree”. IEEE Int Symp Circ Syst, Chicago, IL. 279–282
Said A, Pearlman WA (1996) A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circ Syst Video Technol 6(3):243–250
Selvakumarasamy K, Radhikadevi R, Sagunthala (2013) Performance analysis of bi-orthogonal wavelets for fingerprint image compression. ITSI Trans Electric Electron Eng 1(4):64–70
Shapiro JM (1993) Embedded image coding using zerotrees of wavelets coefficients. IEEE Trans Signal Process 41(12):3445–3462
Sherlock BG, Monro DM (1996) “Optimized wavelets for fingerprint compression”. Proc Int Conf Acoustics, Speech Sign Process (ICASSP’96)
Sherlock BG, Monro DM (1997) “Balanced uncertainty wavelets for fingerprint compression”. IEE Colloquium Image Process Sec Appl 5–8, (London, GB)
Tabassi E, Wilson CL (2005) A novel approach to fingerprint image quality. Int Conf Image Process 2:37–40
Tabassi E, Wilson C, Watson C (2004) “Fingerprint image quality, NFIQ,” National Institute of Standards and Technology, NISTIR 7151 edn
Tian J, Wells RO (1996) “A lossy image codec based on index coding and embedded image coding using wavelet difference reduction,”. Proc IEEE Data Comp Conf (DCC’96). 456,
Walker JS (2001) “Wavelet-based image compression”. transforms and data compression handbook. CRC Press LLC, Boca Raton
Walker JS, Nguyen TO (2000) “Adaptive scanning methods for wavelet difference reduction in lossy image compression,”. Proc IEEE Int Conf Image Process 3, Vancouver, Canada, 182–185
Watson C, Garris M, Tabassi E, Wilson C, McCabe R, Janet S (2004) User’s guide to fingerprint image software 2-NFIS2 [Online]. Available: http//:www.fingerprint.nist.gov/NFIS. NIST
Zhao S, Wang X-F (2009) “Fingerprint image compression based on directional filter banks and TCQ”. Second Int Workshop Knowledge Discov Data Mining 660–663
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Grailu, H. Improving the fingerprint verification performance of set partitioning coders at low bit rates. Multimed Tools Appl 76, 9959–9991 (2017). https://doi.org/10.1007/s11042-016-3590-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-3590-0