FINGERPRINT ENHANCEMEN T BY DIRECTIONAL FILTERING Sreya Chakraborty Under the guidance of Dr K R Rao Multimedia Processing Lab MPL University of Texas at Arlington CONTENTS Introduction Flowchart Normalization Orientation Gabor filtering Result A fingerprint image with marked singularities minutiae and the frequency spectra corresponding to the local regions 1 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 A flowchart of the proposed fingerprint enhancement algorithm 3 The main purpose of normalization is 1 To have images with similar characteristics 2 To remove the effect of the sensor noise Normalized image 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 nonoverlapping blocks 2 For each pixel p of the block the x and y components of the gradient Gx and Gy respectively are calculated The average gradient direction and dominant local orientation for the block are given by 1 1 2tan 2G G G G x y w 2 x w 2 y o i j 2 3 Additional low pass filtering is done in order to eliminate the wrongly estimated ridge Orientation field image 7 Here 8 different values for are used i 8 i 1 2 8 with respect to x axis are used Filtered image for direction 22 50 1 Filtered image for direction 900 1 Oriented window and x signature 3 A 32x16 oriented window centered at x i yj 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 graylevel profile smoother and prevents ridge peaks from being obscured due to small ridge breaks or pores Fij is determined as the inverse of the average distance between two consecutive peaks of the x signature Algorithm for fingerprint enhancement 1 The FFT F of the image I is computed each filter Pi is point by point multiplied by F thus obtaining n filtered image transforms PFi i 1 2 n inverse FFT is computed for each PFi resulting in n filtered images PIi i 1 2 n each enhanced image is obtained by setting for each pixel x y Ien x y PIk x y where k is the index of the filter whose orientation is closest to xy H x y 1 2 x y cos 2 wx x 2 wy y exp 0 5 x x2 y y2 The even symmetric two dimensional Gabor filter has the above form Original figure Image after Gabor filtering ENHANCED IMAGE 7 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 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 Thank you
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