1. Sreya Chakraborty Under the guidance of Dr. K. R. Rao Multimedia Processing Lab (MPL) University of Texas at Arlington

2.  Introduction  Flowchart  Normalization  Orientation  Gabor filtering  Result

3. A fingerprint image with marked singularities, minutiae and the frequency spectra corresponding to the local regions.[1]

4.  Automatic Fingerprint Recognition System relies on the input fingerprint for feature extraction. Hence, the effectiveness of feature extraction relies heavily on the quality of input fingerprint images. In this paper adaptive filtering in frequency domain in order to enhance fingerprint image is proposed.  Several stages of processing take place when an Automated Fingerprint Identification System (AFIS) is used to match an unknown fingerprint.[2]

5. A flowchart of the proposed fingerprint enhancement algorithm [3]

6. Normalized image [7] The main purpose of normalization is : 1) To have images with similar characteristics 2) To remove the effect of the sensor noise

7.  The orientation field O is defined as a PxQ image where O(i,j) represents the local ridge orientation at pixel(i,j).[1] 1) The input image is first divided into a number of non- overlapping blocks 2) For each pixel p of the block the x and y components of the gradient, G x and G y respectively, are calculated.  The average gradient ф direction and dominant local orientation for the block are given by

8. Orientation field image [7] 3) Additional low pass filtering is done in order to eliminate the wrongly estimated ridge.

9. Filtered image for direction 22.5 0 [1] Filtered image for direction 90 0 [1] Here 8 different values for ф are used : ф=i*Π/8 (i=1,2,……,8) with respect to x-axis are used.

10. Oriented window and x-signature [3]

11.  A 32x16 oriented window centered at [x i, y j ] is defined in the ridge co-ordinate systems (i.e., rotated to align the y-axis with the local ridge orientation).  The x-signature of the gray-levels is obtained by accumulating for each column x, the gray-levels of the corresponding pixels in the oriented window. This sort of averaging that makes the gray-level profile smoother and prevents ridge peaks from being obscured due to small ridge breaks or pores.  F ij is determined as the inverse of the average distance between two consecutive peaks of the x-signature.

12. Algorithm for fingerprint enhancement [1]

13.  The FFT F of the image I is computed  each filter P i is point-by-point multiplied by F, thus obtaining n filtered image transforms PF i, i=1,2,…,n  inverse FFT is computed for each PF i resulting in n filtered images PI i, i=1,2,…,n  each enhanced image is obtained by setting for each pixel [x,y], I en [x,y]= PI k [x,y] where k is the index of the filter whose orientation is closest to θ xy

14. Original figureImage after Gabor filtering The even symmetric two dimensional Gabor filter has the above form

15. it is proposed to implement adaptive filtering for fingerprint enhancement. Due to the above mentioned characteristics of the fingerprint in the frequency domain directional filtering is used for the enhancement This technique helps to increase the contrast between the ridges and valleys thereby removing noise from the image.

16.  References: [1] A.M.Raievi and B.M. Popovi, “An Effective and Robust Enhancement by Adaptive Filtering Domain”,SER.:ELEC.ENERG. vol.22, no. 1, pp.91-104 April 2009. [2] B.G. Sherlock, D.M. Monro, and K. Millard, “Fingerprint Enhancement by Directional Fourier Filtering,” IEE Proc. Vision Image Signal Process., vol.141, no. 2, pp. 87-94, April 1994. [3] L. Hong, Y.Wan, and A.K. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evolution,”IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 8, pp. 777-789, Aug. 1998. [4] J.Yang, L. Lin, T. Jiang, and Y.Fan, “A Modified Gabor Filter Design Method for Fingerprint Image Enhancement,” Pattern Recognition Letters, vol. 24, pp. 1805- 1817,Jan. 2003. [5] A.K. Jain and F. Farrokhnia,”Unsupervised Texture Segmentation Using Gabor Filters,” Pattern Recognition, vol. 24, no. 12, pp. 1,167-1,186, May 1991. [6] K. Karu and A.K. Jain, “Fingerprint Classification,” Pattern Recognition, vol.29, no. 3, pp. 389-404, 1996. [7] Database [online]. Availabe http://www.nist.gov/itl/iad/ig/sd27a.cfm. [8] A.L Bovik, Handbook of Image and Video Processing. Elsevier, 2005. [9]K.R.Rao, D.N.Kim and J.J.Hwang, “Fast Fourier Transform:Algorithms and Applications”, Heidelberg, Germany: Springer 2010.

17. Thank you