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Enhancement techniques in fingerprints as a tool in dactyloscopy
Keywords:
Dactyloscopy, Forensic Sciences, digital image, fingerprints, enhancementAbstract
In this work an application of image enhancement techniques in fingerprints is presented. Digital Image-processing technology was applied to restored fingerprints with non-uniform contrast. These fingerprints corresponded to unidentified persons who were murdered by Military Forces in 1976. In 1997, after a court decision, Human Right Organisations acceded to the microfilms of the Computer Records Division of the Province of Buenos Aires Police, which contained fingerprints and complementary information from these missing people. Unfortunately most of the fingerprints were taken with a reduction ratio of 41x, they were blurred and their spatial definition was not clear. These features made their classification and comparison very difficult. In this paper a combination of spatial, frequency and morphological enhancement techniques used to restore the fingerprints is presented. Filters had to be combined because the results obtained by applying conventional enhancement techniques were not satisfactory. The resulting images show prominent features in fingerprints that cannot be extracted by other enhancement techniques.
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References
[1] Comisión Nacional sobre la Desaparición de Personas, Nunca más, Buenos Aires: EUDEBA, 1984.
[2] M. Cohen Salama, Tumbas anónimas: Informe sobre la identificación de restos de desaparecidos en Argentina, Buenos Aires: Catálogos, 1994.
[3] M. Hanson, E. Moler, V. Ballarin, “Fingerprint-based Forensics Identify Argentina’s Desaparecidos“, IEEE Computer Graphics and Applications, 2000, pp. 6-10.
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[6] J. Serra, Image Analysis and Mathematical Morphology, Vol. II, London: Academic Press, 1988.
[7] P. Klaus-Ruediger, “Collection Deficiencies of Scanning Electron Microscopy Signal Contrasts Measured and Corrected by Differential Hysteresis”, Image Processing. Scanning, Vol. 18, 1996, pp. 539-555.
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[10] R. Haralick, L. Shapiro. Computer and Robot Vision, New York : Addison-Wesley, 1992.
[11] V. Fain, V. Tkhor, “Highly reliable fingerprint identification procedure”, Proceedings of the 6th International Conference on Signal Processing: Applications and Technology, Boston, 1995.
[12] J. Serra, Image Analysis and Mathematical Morphology, Vol. I, London: Academic Press, 1982.
[13] E. Dougherty, An Introduction to Nonlinear Image Processing, Washington: SPIE, 1994.
[14] L. Vincent, “Morphological Grayscale Reconstruction in Image Analysis: Applications and efficient Algorithms”, IEEE Transactions On Image Processing, Vol. 2, 1993, pp. 176-201.
[15] F. Zana, J.C. Klein, “Vessel-Like Patterns using Mathematical Morphology and Curvature Evaluation”, IEEE Trans. Image Processing, Vol. 10, pp. 1010-1019.
[16] Image Pro-Plus, “The Proven Solution for Image Analysis. Image Pro-Plus Reference Guide for Windows Version 4.1”, Media Cybernetics, 1999.
[17] SDC, “SDC Morphology Toolbox for MATLAB 5. User’s Guide”, SDC Information Systems, 2001.
[2] M. Cohen Salama, Tumbas anónimas: Informe sobre la identificación de restos de desaparecidos en Argentina, Buenos Aires: Catálogos, 1994.
[3] M. Hanson, E. Moler, V. Ballarin, “Fingerprint-based Forensics Identify Argentina’s Desaparecidos“, IEEE Computer Graphics and Applications, 2000, pp. 6-10.
[4] R. Gonzalez, R. Woods, Digital Image Processing, New York: Adison -Wesley, 1992.
[5] P. Klaus-Ruediger, “Digital differential hystersis image processing displays what the microscope acquires but the eye can’t see”, Fifty-Second Annual Meeting Microscopy Society America, San Francisco Press, Inc., Eds. G.W. Bailey, A.J. Garratt-Reed, 1994, pp. 416-417.
[6] J. Serra, Image Analysis and Mathematical Morphology, Vol. II, London: Academic Press, 1988.
[7] P. Klaus-Ruediger, “Collection Deficiencies of Scanning Electron Microscopy Signal Contrasts Measured and Corrected by Differential Hysteresis”, Image Processing. Scanning, Vol. 18, 1996, pp. 539-555.
[8] S.K. Bramble, G.R. Jackson, “Operational experience of fingermark enhancement by frequency domain filtering”, J Forensic Sci, Vol. 39, No. 4, Jul 1994, pp. 920-32.
[9] E. Moler, V. Ballarin, F. Pessana, S. Torres, D. Olmo, “Fingerprint Identification using Image EnhancementTechniques”, J Forensic Sci, Vol. 43, No. 3, 1998, pp. 689-692.
[10] R. Haralick, L. Shapiro. Computer and Robot Vision, New York : Addison-Wesley, 1992.
[11] V. Fain, V. Tkhor, “Highly reliable fingerprint identification procedure”, Proceedings of the 6th International Conference on Signal Processing: Applications and Technology, Boston, 1995.
[12] J. Serra, Image Analysis and Mathematical Morphology, Vol. I, London: Academic Press, 1982.
[13] E. Dougherty, An Introduction to Nonlinear Image Processing, Washington: SPIE, 1994.
[14] L. Vincent, “Morphological Grayscale Reconstruction in Image Analysis: Applications and efficient Algorithms”, IEEE Transactions On Image Processing, Vol. 2, 1993, pp. 176-201.
[15] F. Zana, J.C. Klein, “Vessel-Like Patterns using Mathematical Morphology and Curvature Evaluation”, IEEE Trans. Image Processing, Vol. 10, pp. 1010-1019.
[16] Image Pro-Plus, “The Proven Solution for Image Analysis. Image Pro-Plus Reference Guide for Windows Version 4.1”, Media Cybernetics, 1999.
[17] SDC, “SDC Morphology Toolbox for MATLAB 5. User’s Guide”, SDC Information Systems, 2001.
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Published
2003-10-01
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[1]
“Enhancement techniques in fingerprints as a tool in dactyloscopy”, JCS&T, vol. 3, no. 02, pp. p. 52–55, Oct. 2003, Accessed: Mar. 08, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/939
