The week's pick
Random Articles
Reseach Article
Fingerprint Image Enhancement based on Threshold Fast Discrete Curvelet Transform (FDCT) and Gabor Filters
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 110 - Number 3 |
| Year of Publication: 2015 |
| Authors: Hany Hashem Ahmed, Hamdy M. Kelash, Maha Tolba, Mohamed Badwy |
10.5120/19299-0746
|
Hany Hashem Ahmed, Hamdy M. Kelash, Maha Tolba, Mohamed Badwy . Fingerprint Image Enhancement based on Threshold Fast Discrete Curvelet Transform (FDCT) and Gabor Filters. International Journal of Computer Applications. 110, 3 ( January 2015), 33-41. DOI=10.5120/19299-0746
Abstract
The most important stages of Automatic Fingerprint Identification System (AFIS) are enhancement stage, features extraction stage, and matching stage. The main purpose of the enhancement stage is to increase the clarity of the fingerprint image, convert poor quality image to good quality image, and prepare the image for features extraction stage. Both of the two last stages (features extraction & matching stage) depend heavily on the enhancement stage, therefore this paper focus on the enhancement stage. Practically most of the input fingerprint images are corrupted by noise, body conditions, and environmental factors. Therefore it is necessary to use an effectively enhancement method. This paper present new efficient fingerprint image enhancement algorithm works by performing threshold on Fast Discrete Curvelet Transform (FDCT) domain and applying Gabor Filters. The proposed algorithm reduce the fingerprint image noise by using threshold fast discrete curvelet transform, then apply Gabor filters to enhance and increase the clarity of the image. The performance of proposed enhancement method is evaluated on the basis of Peak Signal to Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE) and the Computation Time (CT). Experimental results, and the comparison table between proposed enhancement algorithm against traditional methods at the end of this paper show that the proposed method is computationally efficient, with the same level of the enhancement performance. The proposed enhancement method is compared with other traditional fingerprint enhancement techniques. The performance is evaluated on the basis of Peak Signal to Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE) and the Computation Time (CT). Experimental results show that the proposed method is computationally efficient, with the same level of the enhancement performance.
References
- D. Davide Maltoni, D. Dario Maio, Anil K. Jain, and Salil Prabhakar, “Handbook of Fingerprint Recognition Second Edition,” New York: Springer-Verlag, 2009.
- http://en.wikipedia.org/wiki/Automated_Fingerprint_Identification_System
- Zain S. Barham, Supervised by Dr. Allam Mousa, " Fingerprint Recognition using MATLAB ," Graduation project 5/17/2011
- http://en.wikipedia.org/wiki/Histogram_equalization
- Ekta Thirani, “A Comparative Analysis of Fingerprint Enhancement Technique through Absolute Mean Brightness Error and Entropy, ” IJRITCC, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2277- 4804 Volume: 1 Issue: 2 | JAN 2013
- http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
- M.Manju, and V.Kavitha "Survey on Fingerprint Enhancement Techniques," International Journal of Computer Applications (0975 – 8887) Volume 62– No.4, January 2013.
- Aliaa A.A.Youssif, A.A.Darwish, and A.M.M.Madbouly, “Adaptive Algorithm for Image Denoising Based on Curvelet Threshold, ” IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.1, January 2010
- E. J. Candès and D. L. Donoho, “Curvelets,” [Online] Available: http://www-stat.stanford.edu/~donoho/Reports/1999/curvelets.pdf.
- E.Cand`es, L.Demanet, D.Donoho and L.Ying, "Fast Discrete Curvelet Transforms", March 2006. http://www.curvelet.org/papers/FDCT.pdf
- J.Candes, L.Demanet, D. Donoho and .Ying. http://www.curvelet.org.
- Iwasokun Gabriel Babatunde, Akinyokun Oluwole Charles, Alese Boniface Kayode, and Olabode Olatubosun "Fingerprint Image Enhancement: Segmentation to Thinning," (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 1, 2012
- Somsak Choomchuay, and Keokanlaya Sihalath,“An Application of Second Derivative of Gaussian Filters in Fingerprint Image Enhancement, ” 978-1-4244-4713-8/10/ ©2010 IEEE.
- http://bias.csr.unibo.it/fvc2002/download.asp
- http://www.mathworks.com
- http://www.imageprocessingbasics.com/
- N. Phanthuna, F. Cheevasuvit and S. Chitwong, "CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING," ASPRS 2009 Annual Conference Baltimore, Maryland, March 9-13, 2009.
Index Terms
Keywords