Quality Assessment and Identification of Fingerprints by Analysis of the Image’s Structural Properties
https://doi.org/10.30764/1819-2785-2020-4-90-97
Abstract
The paper addresses the problem of assessing the quality of fingerprint images using spatial analysis methods. The author proposes using the previously developed mathematical model to describe the set of magnitudes of the image gradient. The model is based on the two-parameter Weibull distribution. The author proposes two approaches to assess the quality of fingerprints. The first approach is implemented by the so-called “Full reference method”, which compares the Weibull distribution parameters’ values of statistical estimates. The results of identifying sweat pores using this method are presented. The second approach is called the “No-Reference method” and is used to assess fingerprints’ quality when analyzing and identifying the information content of their individual sections. It is proposed to use an image blur map as a quality characteristic and a statistical estimate of the Weibull distribution shape parameter as a measure of the blur. The shape parameter is estimated at each image point by the combination of magnitudes of the image gradient in the vicinity of the point; in this, the previously developed blur mapping technique is applied. The specific examples illustrate effectiveness of the proposed approaches.
About the Author
D. G. AsatryanArmenia
Asatryan David Geghamovich – Doctor of Engineering, Leading Scientist of the Institute for Informatics and Automation Problems of National Academy of Sciences of Armenia, Head of the Research Center for Critical Technologies of Russian-Armenian University
Yerevan 0014
Yerevan 0052
References
1. Olsen M.A., Šmida V., Busch C. Finger Image Quality Assessment Features – Definitions and Evaluation. IET Biometrics. 2016. Vol. 5. No. 2. P. 47–64. https://doi.org/10.1049/iet-bmt.2014.0055
2. Yao Zh., Le Bars J., Charrier C., Rosenberger C. Literature Review of Fingerprint Quality Assessment and Its Evaluation. IET Biometrics. 2016. Vol. 5. No. 3. P. 243–251. https://doi.org/10.1049/iet-bmt.2015.0027
3. Alonso-Fernandez F., Fierrez J., Ortega- Garcia J., Gonzalez-Rodriguez J., Fronthaler H., Kollreider K., Bigun J. A Comparative Study of Fingerprint Image-Quality Estimation Methods. IEEE Transaction on Information Forensics and Security. 2007. Vol. 2. No. 4. P. 734–743. https://doi.org/10.1109/tifs.2007.908228
4. Kanjan N., Patil K., Ranaware S., Sarokte P. A Comparative Study of Fingerprint Matching Algorithms. International Research Journal of Engineering and Technology. 2017. Vol. 4. No. 11. P. 1892–1896.
5. Chen T., Jiang X., Yau W. Fingerprint Image Quality Analysis. 2004 IEEE International Conference on Image Processing (ICIP 2004). (Singapore, October 24–27, 2004). IEEE, 2004. Vol. 2. P. 1253–1256. https://doi.org/10.1109/icip.2004.1419725
6. Shen L., Kot A., Koo W. Quality Measures of Fingerprint Images. In: Bigun J., Smeraldi F. (eds). Audio- and Video-Based Biometric Person Authentication. Third International Conference, AVBPA 2001 (Halmstad, Sweden, June 6–8, 2001). Proceedings. Lecture Notes in Computer Science. 2001. Vol. 2091. P. 266–271. https://doi.org/10.1007/3-540-45344-x_39
7. Asatryan D., Egiazarian K. Quality Assessment Measure Based on Image Structural Properties. 2009 International Workshop on Local and Non-Local Approximation in Image Processing (Tuusula, Finland, August 19–21, 2009). IEEE. 2009. P. 70–73. https://doi.org/10.1109/lnla.2009.5278400
8. Asatryan D.G. Gradient-Based Technique for Image Structural Analysis and Applications. Computer Optics. 2019. Vol. 43. No. 2. P. 245–250. https://doi.org/10.18287/2412-6179-2019-43-2-245-250
9. Asatryan D.G. Image Blur Estimation Using Gradient Field Analysis. Computer Optics. 2017. Vol. 41. No. 6. P. 957–962. (In Russ.). https://doi.org/10.18287/2412-6179-2017-41-6-957-962
10. Geusebroek J.-M. The Stochastic Structure of Images. In: Kimmel R., Sochen N.A., Weickert J. (eds). Scale Space and PDE Methods in Computer Vision. Scale-Space 2005. Lecture Notes in Computer Science. 2005. Vol. 3459. P. 327–338. https://doi.org/10.1007/11408031_28
11. Yanulevskaya V., Geusebroek J.-M. Significance of the Weibull Distribution and its Sub-models in Natural Image Statistics. Proceedings of the Fourth International Conference on Computer Vision Theory and Applications (Lisboa, Portugal). 2009. Vol. 1. P. 355–362. https://doi.org/10.5220/0001793203550362
12. Gonzalez R., Woods R. Digital Image Processing. 3rd ed. Moscow: Tekhnosfera, 2012. 1104 p. (In Russ.)
13. Jain A.K., Chen Y., Demirkus M. Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features. 18th International Conference on Pattern Recognition (ICPR’06). (Hong Kong, China, August 20–24, 2006). IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007. Vol. 29. No. 1. P. 15–27. https://doi.org/10.1109/icpr.2006.938
14. Maltoni D., Maio D., Jain A., Prabhakar S. Handbook of Fingerprint Recognition. New York: Springer, 2003. 494 p. https://doi.org/10.1007/978-1-84882-254-2
15. Asatryan D., Sazhumyan G., Sakanyan B. New Technique for Analysis of Fingerprint Poroscopical Map. Proceedings of 9th International Conference on Computer Science and Information Technologies – CSIT’2013. Yerevan: IIAP, 2013. P. 181–184.
16. Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Transaction on Systems, Man, and Cybernetics. 1979. Vol. 9. No. 1. P. 62–66. https://doi.org/10.1109/tsmc.1979.4310076
Review
For citations:
Asatryan D.G. Quality Assessment and Identification of Fingerprints by Analysis of the Image’s Structural Properties. Theory and Practice of Forensic Science. 2020;15(4):90-97. (In Russ.) https://doi.org/10.30764/1819-2785-2020-4-90-97