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A Comprehensive Performance Analysis of GLCM-DWT-based Classification on Fingerprint Identification

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Taner Çevik, Ali Mustafa Ali Alshaykha, Nazife Çevik

Taner Çevik, Ali Mustafa Ali Alshaykha and Nazife Çevik. A Comprehensive Performance Analysis of GLCM-DWT-based Classification on Fingerprint Identification. International Journal of Computer Applications 180(32):42-47, April 2018. BibTeX

	author = {Taner Çevik and Ali Mustafa Ali Alshaykha and Nazife Çevik},
	title = {A Comprehensive Performance Analysis of GLCM-DWT-based Classification on Fingerprint Identification},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2018},
	volume = {180},
	number = {32},
	month = {Apr},
	year = {2018},
	issn = {0975-8887},
	pages = {42-47},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2018916909},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Fingerprint detection is one of the primary methods for identifying individuals. Gray Level Co-occurrence Matrix (GLCM) is the oldest and prominent statistical textual feature extraction method applied in many fields for texture analysis. GLCM holds the distribution of co-occurring intensity patterns at a given offset over a given image. However, images occupy excessive space in storage by its original sizes. Thus, Discrete Wavelet Transform (DWT) based compression has become popular especially for reducing the size of the fingerprint images. It is important to investigate whether GLCM-based classification can be utilized efficiently on DWT-compressed fingerprint images. In this paper, we analyze the performance of GLCM-based classification on DWT-compressed fingerprint images. We performed satisfying simulations for different levels of DWT-compressed images. Simulation results identify that classification performance sharply decreases by the increase of DWT-compression level. Besides, instead of utilizing all Haralick features, it is recognized that eight of them are the most prominent ones that affect the accuracy performance of the classification.


  1. Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems. .
  2. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  3. Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  4. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  5. Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
  6. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
  7. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
  8. Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
  9. Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender


Fingerprint Detection, GLCM, DWT, Classification.