International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 7, No. 11, 8 Oct. 2015
Cover page and Table of Contents: PDF (size: 683KB)
Performance Analysis of Fingerprint Denoising Using Stationary Wavelet Transform
Full Text (PDF, 683KB), PP.48-54
Views: 0 Downloads: 0
Author(s)
Usha.S
1,*
Kuppuswami.S
1
Index Terms
Denoising, Fingerprint, Normal Shrink, Visu Shrink, Quality Metrics, Stationary Wavelet Transform
Abstract
Finger print is the finest and cheapest recognition system because of its easy extraction of unique features like bifurcation and termination. But the quality of fingerprint data are easily degraded by dryness of skin, wet, wound and other types of noises. Hence, denoising of fingerprint image is vital step for automatic fingerprint recognition system. In the proposed paper the removal of noise from fingerprint images by using stationary wavelet transform and adaptive thresholding method is analysed. The proposed algorithm is developed using MATLAB (R2010b) and tested in the fingerprint images collected from FVC2004 database and R303A optical scanner. The performance of the method is analysed by calculating the quality metrics like Peak Signal to Noise Ratio, Universal Quality Index , Structure Similarity and Multi-Scale Structure Similarity (MS-SSIM). The quality of fingerprint image after noise removal using proposed analysis confirms the suggested method is better than the conventional techniques.
Cite This Paper
Usha.S, Kuppuswami.S,"Performance Analysis of Fingerprint Denoising Using Stationary Wavelet Transform", IJIGSP, vol.7, no.11, pp.48-54, 2015. DOI: 10.5815/ijigsp.2015.11.07
Reference
[1]S.S . Gornale , V. Humbe, R. Manza and K. Kale, " Fingerprint image de-noising using multiresolution analysis through SWT method", International Journal of Knowledge Engineering, vol. 1, no. 1, pp.05-14, 2010.
[2]Zin Mar Win and Myint Myint Sein, "An Efficient Finger Print Matching System for Low Quality Images", International Journal of Computer Applications, vol. 26, no. 4, pp. 5-12, 2011.
[3]Alle Meije Wink and Jos B.T.M.Roerdink, "Denoising Functional MR Images; A Comparison of Wavelet Denoising And Gaussian Smoothing", IEEE Transactions on Medical Imaging, vol. 23, no.3, pp.374-387, 2004.
[4]H.A Garcia-Baleon, V. Alarcon-Aquino, and J. F Ramirez-Cruz, "Bimodal Biometric System for Cryptographic Key Generation Using Wavelet Transforms", in Proceedings of the IEEE Mexican International Conference on Computer Science, ENC 2009, Sep. 2009.
[5]Jean-Luc Stark, Jalal Fadili and Fionn Murtagh "The Undecimated Wavelet Decomposition and its Reconstruction", IEEE Transactions on Image Processing, vol 16, no 2, pp 297-309.
[6]Iman Elyasi, and Sadegh Zarmehi," Elimination Noise by Adaptive Wavelet Threshold", World Academy of Science, Engineering and Technology 56 2009, pp.462-466.
[7]E. Chandra and K.Kanagalakshmi, "Noise Elimination in Fingerprint Image Using Median Filter", International Journal on Advanced Networking and Applications, vol. 2, no. 06, pp.950-955, 2011.
[8]M.Raghuveer Rao, and A.S. Bopardikar, "Wavelet Transforms: Introduction to Theory and application", Addison-Wesley, pp 1-126, 2001.
[9]Hani.M.Ibrahem, " An efficient switching filter based on cubic B-spline for removal of salt and pepper noise ", International Journal of Image, Graphics and Signal Processing ,vol.6, no.5, pp.45-52, 2014.
[10]Nagaraju and S.S.Parthasarathy, "Analysis and estimation of noise in embedded medical images", International Journal of Image, Graphics and Signal Processing, vol.7, no.3, pp.45-50, 2015.
[11]S. Grace Chang, Bin Yu and Martin Vetterli "Adaptive Wavelet Thresholding for Image Denoising and Compression", IEEE transactions on Image Processing, vol. 9, no 9, pp. 1532-1546, 2000.
[12]Zhou Wang and Alan C. Bovik, "A Universal Image Quality Index", IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, 2002.
[13]Zhou Wang, Alan C. Bovik, Hamid R. Sheikh and Eero P. Simoncelli " Image Quality Assessment : From Error Visibility to structural similarity", IEEE Transaction Image Processing, vol .13, no 4, pp 1-14, 2004.
[14]Zhou Wang, Eero P.Simoncelli and Alan C.Bovik, "Multi-Scale Structural Similarity For Image Quality Assessment', Proceedings of 37th IEEEAsilomar Conference on Signals,Systems and Computers",Pacific Grove, Nov 9-12,2003.
[15]Sachin D Ruikar, Dharmpal D Doye, "Wavelet Based Image Denoising Technique', International Journal of Advanced Computer Science and Applications, vol. 2, no.3, pp 49-53, 2011.
[16]Arun Dixit and Poonam Sharma, " A Comparative study of wavelet thresholoding for image denoising", Internation Journal of Image, Graphics and Signal Processing ,vol.6 no.12, pp.39-46, 2014.
[17]A.K.Jain, H.Lin, S.Pankanti and R.Bolle., " An Identity Authentication System Using Fingerprints", Proceedings of the IEEE, vol. 85, no. 9, 1997.
[18]D.L Donoho and I.M. Johnstone, "Denoising by Soft Thresholding", IEEE Transaction on Information Theory, vol.41, 1995, pp 613-627.