Advertisement

International Journal of Information Technology

, Volume 11, Issue 4, pp 767–772 | Cite as

Various image enhancement and matching techniques used for fingerprint recognition system

  • Munish KumarEmail author
  • Priyanka
Original Research

Abstract

For biometric identification or verification fingerprint images are most popular due to their uniqueness in nature. Image Enhancement Techniques (IETs) plays a vital role in Fingerprint Recognition (FPR) System and IETs are one of the most important stages in FPR system. Fingerprint images suffer problems related to brightness, poor contrast and blurring due to noise and motion etc. Fingerprint images may be corrupted and degraded due to variation in environmental conditions, skin, pressure on the sensors, and various other impression conditions. To overcome these problems, IETs are used. The main aim of implementing IET to the input image so that the visual quality or information contents are more suitable for a specific application or automated image processing. The performance of FPR system relies on the matching techniques that depend on the input fingerprint image quality and algorithm used. Depending upon the matching process there are various FPR system matching techniques. Enhancing the fingerprint images by IETs provide more reliable feature extraction information for the matching process. This paper presents an overview of various IETs used for FPR system, then types, applications and role in FPR system for researchers. A method is proposed which uses these techniques for better performance of the FPR system by improving the quality of fingerprint images using IETs.

Keywords

IET FPR system Fingerprint Images 

References

  1. 1.
    Kush A, Hwang C (2011) Hash security for ad hoc routing. In: BIJIT—BVICAM’s International Journal of Information Technology Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi Copy Right BIJIT; January–June 2011 3(1) (ISSN 0973–5658 271)Google Scholar
  2. 2.
    Daugman J (1994) Biometric personal identification system based on iris analysis. United States Patent, 5291560Google Scholar
  3. 3.
    Singh D, Singh A (2010) A secure private key encryption technique for data security in modern cryptosystem. In: BIJIT—BVICAM’s International Journal of Information Technology Bharati Vidyapeeth’s Institute of Computer Applications and Management BVICAM), New DelhiGoogle Scholar
  4. 4.
    Masek L (2003) Recognition of human iris patterns for biometric identification. University of Western Australia, PerthGoogle Scholar
  5. 5.
    Panganiban A, Linsangan N, Caluyo F (2011) Wavelet-based feature extraction algorithm for an iris recognition system. J Inf Process Syst 5(3):425–434CrossRefGoogle Scholar
  6. 6.
    Rajput S, Suralkar SR (2013) Comparative study of image enhancement techniques. IJCSMC 2(1):11–21Google Scholar
  7. 7.
    Gandhi S et al (2013) A novel Hé non- map based adaptive PSO for wavelet shrinkage image denoising. In: BIJIT—BVICAM’s International Journal of Information Technology Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA)Google Scholar
  8. 8.
    Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  9. 9.
    Ak Jain (1989) Fundamental of digital image processing. Prentice Hall International, Upper Saddle RiverGoogle Scholar
  10. 10.
    Hung DDC (1993) Enhancement and feature purification of fingerprint images. Pattern Recognit 26(11):1661–1671CrossRefGoogle Scholar
  11. 11.
    Sherlock BG, Munro DM, Millard K (1994) Algorithm for enhancing fingerprint images. Electron Lett 28(18):1720–1721CrossRefGoogle Scholar
  12. 12.
    Jain A, Hong L, Pankanti S, Bolle R (1997) An identity authentication system using fingerprints. Proc IEEE 85:1365–1388CrossRefGoogle Scholar
  13. 13.
    Hong L, Wan Y, Jain A (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789CrossRefGoogle Scholar
  14. 14.
    Sagar VK, Alex KJB (1999) Hybrid fuzzy logic and neural network model for fingerprint minutiae extraction. IEEE Transa 7803:3255–3259Google Scholar
  15. 15.
    Greenberg S, Aladjem M, Kogan D and Dimitrov I (2000) Fingerprint image enhancement using filtering techniques. ICPR 3:326–329zbMATHGoogle Scholar
  16. 16.
    Kim B-G, Kim H-J, Park D-Jo (2002) New enhancement algorithm for fingerprint images. IEEE Proc 4651(2):1051–1055Google Scholar
  17. 17.
    Yang J, Liu L, Jiang T, Fan Y (2003) An improved method for extraction of fingerprint features. In: Proc. 2nd ICIG, SPIE, vol 4875, no. 1, pp 552–558Google Scholar
  18. 18.
    C Wu, Shi Z, Govindaraju V (2004) Fingerprint image enhancement method using directional median filter. Elsevier Science, Amsterdam, pp 250–256Google Scholar
  19. 19.
    Wang S (2004) Fingerprint enhancement in the singular point area. IEEE Signal Process Lett 11(1):16–19CrossRefGoogle Scholar
  20. 20.
    Blotta E (2004) Fingerprint image enhancement by differential hysteresis processing, vol 141. Elsevier Ireland Ltd., Forensic Science International, Amsterdam, pp 109–113Google Scholar
  21. 21.
    Mohd MAU, Khan MK, Khan MA (2005) Fingerprint image enhancement using decimation-free directional filter bank. Inf Technol J 4(1):16–20CrossRefGoogle Scholar
  22. 22.
    Yun E-K, Cho S-B (2006) Adaptive fingerprint image enhancement with fingerprint image quality analysis, vol 24. Elsevier, Image and Vision Computing, Amsterdam, pp 101–110Google Scholar
  23. 23.
    Bartunek JS, Nilsson M, Nordberg J and Claesson I (2006) Adaptive fingerprint binarization by frequency domain analysis. IEEE Trans pp 598–602Google Scholar
  24. 24.
    Ercelebi E and Koc S (2006) Lifting-based wavelet domain adaptive Wiener filter for image enhancement. In: IEE Proc.-Vis. Image Signal Process, vol. 153, No. 1, February 2006, IEE Proceedings online no. 20045116Google Scholar
  25. 25.
    Chikkerur SS, Cartwright AN, Govindaraju V (2007) Fingerprint image enhancement using STFT. Anal Pattern Recognit 40:198–211CrossRefGoogle Scholar
  26. 26.
    Fronthaler H, Kollreider K, Bigun J (2007) Pyramid-based image enhancement of fingerprints. Halmstad University, HalmstadCrossRefGoogle Scholar
  27. 27.
    Sepasian M, Balachandran W, Mares C (2008) Image enhancement for fingerprint minutiae based algorithms Using CLAHE, standard deviation analysis, and sliding neighborhood. WCECS pp 1199–1203Google Scholar
  28. 28.
    Mandal T and JWu QMJ (2008) A small scale fingerprint matching scheme using digital curvelet transform. In: IEEE Conf. on SMC, pp 1534–1538Google Scholar
  29. 29.
    Yang JC, Park DS (2008) A fingerprint verification algorithm using tessellated invariant moment features. Elsevier, AmsterdamCrossRefGoogle Scholar
  30. 30.
    Fronthaler H, Kollreider K, Bigun J (2008) Local features for enhancement and minutiae extraction in fingerprints. IEEE Trans Image Process 17(3):354–363MathSciNetCrossRefGoogle Scholar
  31. 31.
    Jun-tao X, Zheng-Huang Liu LLJ (2009) An enhancement algorithm for low quality fingerprint image based on edge filter and Gabor filter. In: Proc. of SPIE, vol. 7383Google Scholar
  32. 32.
    Raicevic AM and Popovic BM (2009) An effective and robust finger-print enhancement by adaptive filtering in frequency domain. In: FACTA Universities conference, vol. 22, No. 1, pp 91–104Google Scholar
  33. 33.
    Sutthiwichaiporn P, Areekul V and S. Jirachaweng S (2010) Iterative fingerprint enhancement with matched filtering and quality diffusion in spatial-frequency domain. ICPR pp 1257–1260.  https://doi.org/10.1109/ICPR.2010.313
  34. 34.
    Yoon S, Feng J, Jain AK (2010) On latent fingerprint enhancement. Michigan State University, East LansingCrossRefGoogle Scholar
  35. 35.
    Choudhary J, Sharma S, Verma JS (2011) A new framework for improving low-quality fingerprint images. IJCTA 2(6):1859–1866Google Scholar
  36. 36.
    Nandini C, Ravikumar CN (2011) Improved fingerprint image representation for recognition, vol 1, issue 2. IJCSIT, MIT Publication, India, pp 59–64Google Scholar
  37. 37.
    Bennet D, Perumal AS (2011) Fingerprint: DWT, SVD based enhancement, and significant contrast for ridges and valleys using fuzzy measures. JCSE 6(1):36–42Google Scholar
  38. 38.
    Saeed A, Tariq and Jawaid U (2011) Automated system for fingerprint image enhancement using improved segmentation and Gabor wavelets. In: Information And Communication Technologies (Icict), 2011, Publisher: IEEE, 23–24 July 2011 pp 1–6 (ISBN: 978-1-4577-1553-2)Google Scholar
  39. 39.
    Srinivasan K, Chandrasekar C (2012) An efficient fuzzy based filtering technique for fingerprint image enhancement. AJSR 43:125–140 (ISSN 1450223X) Google Scholar
  40. 40.
    Misra DK, Tripathi SP (2012) Fingerprint image enhancement based on energy minimisation principle. IJCSC 3(1):165–170Google Scholar
  41. 41.
    Babatunde IG, Charles AO, Kayode AB, Olatubosun O (2012) Fingerprint image enhancement: segmentation to thinning. IJACSA 3(1):15–24Google Scholar
  42. 42.
    Yang J, Xiong N, Vasilakos AV (2013) Two stage enhancement scheme for low-quality fingerprint images by learning from images. IEEE Trans Hum Mach Syst 43(2):235–248CrossRefGoogle Scholar
  43. 43.
    Bartunek JS, Nilsson M, Sällberg B, Claesson I (2013) Adaptive fingerprint image enhancement with emphasis on preprocessing of data. IEEE Trans Image Process 22(2):644–656MathSciNetCrossRefGoogle Scholar
  44. 44.
    Mei Y, Zhao B, Zhou Y, Chen S (2014) Orthogonal curved-line Gabor filter for fast fingerprint enhancement. Electron Lett 50(3):175–177CrossRefGoogle Scholar
  45. 45.
    Divya V (2014) Adaptive fingerprint image enhancement based on spatial contextual filtering and preprocessing of data. IJCAT I J of Comput Technol 1(4):56–65 (ISSN: 2348–6090) MathSciNetGoogle Scholar
  46. 46.
    Bouaziz A, Draa A and Chikhi S (2014), A cuckoo search algorithm for fingerprint image contrast enhancement (978-1-4799-4647-1/14/$31.00 ©2014 IEEE)Google Scholar
  47. 47.
    Gayathri S and Sridhar V (2014) ASIC implementation of image enhancement technique for fingerprint recognition process. In: International Conference on contemporary computing and informatics (IC3I), pp 868–873 (978-1-4799-6629-5/14/$31.00 c 2014 IEEE)Google Scholar
  48. 48.
    Chopra J, Upadhyay PC (2012) various fingerprint enhancements and matching technique. Int J Electron Commun Eng 5(3):279–289 (ISSN 0974-2166) Google Scholar
  49. 49.
    Pranav M et al (2010) Simulation and proportional evaluation of AODV and DSR in different environment of WSN. In: BIJIT—BVICAM’s International Journal of Information Technology Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New DelhiGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2017

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringDeenbandhu Chhotu Ram University of Science and TechnologyMurthalIndia

Personalised recommendations