Vol 15, No 1 (2011) > Articles >

Determining the Standard Value of the Oily Distortion of Acquisition the Fingerprint Images

Rahmat Syam 1 , Mochamad Hariadi 2 , Mauridhi Purnomo 2

Affiliations:

  1. Jurusan Matematika, Fakultas MIPA, Universitas Negeri Makassar, Makassar 90222, Indonesia
  2. Laboratorium Multimedia, Teknik Elektro, Fakultas Teknologi Industri, ITS, Sukolilo Surabaya 60111, Indonesia

 

Abstract: This research describes a novel procedure for determining the standard value of the oily distortion of acquisition the fingerprint images based on the score of clarity and ridge-valley thickness ratio. The fingerprint image is quantized into blocks size 32 x 32 pixels. Inside each block, an orientation line, which perpendicular to the ridge direction, is computed. The center of the block along the ridge direction, a two-dimension (2-D) vector  V1 (slanted square) with the pixel size 32 x 13 pixels can be extracted and transformed to a vertical 2-D vector V2. Linear regression can be applied to the one-dimension (1-D) vector V3 to find the determinant threshold (DT1). The lower regions than DT1 are the ridges, otherwise are the valleys. Tests carried out by calculating the clarity  of the image from the overlapping area of the gray-level distribution of ridge and valley that has been separated. Thickness ratio size of the ridge to valley, it is computation per block, the thickness of ridge and valley obtained from the gray-level values per block of image in the normal direction toward the ridge, the average values obtained from the overall image. The results shown that the standard value of the oily distortion of acquisition the fingerprint image is said to oily fingerprint when the images have local clarity scores (LCS) is between 0.01446 to 0.01550, global clarity scores (GCS) is between 0.01186 to 0.01230, and ridge-valley thickness ratio (RVTR) is between 6.98E-05 to 7.22E-05.
Keywords: acquisition, clarity score, distortion, fingerprint images
Published at: Vol 15, No 1 (2011) pages: 55-62
DOI:

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References:

A.K. Jain, A. Ross and S. Prabhakar, IEEE Trans. Circuits Syst. Video Technol. 14/1 (2004) 4.

L.C. Jain, U. Halici, I. Hayashi, S.B. Lee, S. Tsutsui, Intelligent Biometric Techniques in Fingerprint and Face Recognition, CRC Press, USA, 1999, p.34.

L. Ji, Z. Yi, J. Pattern Recognit. 41 (2008) 1491.

E.K. Yun, S.B. Cho, Image Vision Comput. 24/1 (2006) 101.

E. Lim, X. Jiang, IEEE International Conference on Image Processing (ICIP), New York, USA, 1 (2002) 469.

E. Blotta, E. Moler, Forensic Sci. Int. 141 (2004) 109.

L.L. Shen, A. Kot, W.M. Koo, Proc. Of the 3rd AVBPA, London, UK, 2001, p.182.

H.C. Lee, R.E. Gaensslen, Advances in Fingerprint Technology, CRC Press, Boca Raton, FL, 2001, p.274.

T.P. Chen, X. Jiang, X, W.Y. Yau, Proceedings of Conference on Image Process, Singapore, 2 (2004) 1253.

V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998, p.736.

C.W. Hsu, C.J. Lin, IEEE Trans. Neural Networks 13/4 (2002) 415.

J. Wang, P. Neskovic, L.N. Cooper, Studies in Computational Intelligence 35 (2007) 61.

T. Fawcett, Pattern Recognit. Lett. 27 (2006) 861.

D.J. Hand, R.J. Till, J. Machine Learning 45/2 (2001) 171.

M.S. Khalil, D. Mohamad, M.K. Khan, Q. AlNuzaili, Digital Signal Process, Elsevier Science, 20/4 (2010) 1264.