Determining the Standard Value of Acquisition Distortion of Fingerprint Images Based on Image Quality

Rahmat Syam, Mochamad Hariadi, Mauridhi Herry Purnomo

Abstract


This paper describes a novel procedure for determining the standard value of acquisition distortion of fingerprint images. Knowledge about the standard value of acquisition distortion of the fingerprint images is very important in determining the method for improving image quality. In this paper, we propose a model to determine the standard value that can be used in classifying the type of distortion of the fingerprint images based on the image quality. The results show that the standard value of acquisition distortion of the fingerprint images based on the image quality have values of the local clarity scores (LCS) follows: dry parameter values are in the range of 0.0127-0.0149, neutral parameter values are less than 0.0127, and oily parameter values are greater than 0.0149. Meanwhile, the global clarity scores (GCS) are as follows: dry parameter values are in the range of 0.0117-0.0120, neutral parameter values are less than 0.0117, and oily parameter values are greater than 0.0120; and ridge-valley thickness ratios (RVTR) are as follows: dry parameter values are less than 7.75E-05, neutral parameter values are 7.75E-05-5.94E-05, and oily parameter values are greater than 5.94E-05.

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References


Lim, E., Jiang, X.D. & Yau, W.Y., Fingerprint Quality and Validity Analysis, Proc. IEEE Int. Conf. On Image Processing, ICIP, 2000.

Hong, L., Wan, Y. & Jain, A.K., Fingerprint Image Enhancement: Algorithm and Performance Evaluation, IEEE Transaction on Pattern Analysis and Machine Intelligence, 20(8), 1998.

Bolle, et al., System and method for determining the quality of fingerprint images, United State Patent number, US596356, 1999.

Shen, L.L., Kot, A. & Koo, W.M, Quality Measures of Fingerprint Images, 3rd International Conference AVBPA, pp. 182-271, 2001.

Almansa, A. & Lindeberg, T., Fingerprint Enhancement by Shape Adaptation of Scale-Space Operators with Automatic Scale Selection, IEEE Transactions on Image Processing, 9(12), 2027-2042, 2000.

Chen, T.P., Jiang, X., & Yau, W.Y., Fingerprint Image Quality Analysis, 0-7803-8554-3/04/IEEE, 2004.

Syam, R. & Hariadi, M., Adaptive Fingerprint Image Defect Detection and Classification Based on Fingerprint Image Quality Analysis, Proc. Rural Information and Communication Technology International Conference On Image Processing, r-ICT ITB Bandung, pp. 424-428, June, 2009.

Lee, H.C. & Gaensslen, R.E., Advances in Fingerprint Technology, CRC Press, 2001.

Yun, E.K. & Cho, S.B., Adaptive Fingerprint Image Enhancement with Fingerprint Image Quality Analysis, Elsevier on Image and Vision Computing, 24, 101-110, 2006.

Mehtre, B.M., Fingerprint Image Analysis for Automatic Identification, Machine Vision and Applications, 6(2), 124–139, 1993.

Julasayvake, A. & Choomchuay, S., An Algorithm for Fingerprint Core Point Detection, 1-4244-0779-6/07/IEEE, 2007.




DOI: http://dx.doi.org/10.5614%2Fitbj.ict.2010.4.2.4

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