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Fingerprint Image Enhancement

Fingerprint Image Enhancement. Joshua Xavier Munoz-Ramos. Motivation. Method for fingerprint image enhancement Ridge structure in fingerprint images are not always well defined; therefore, enhancement algorithm, is necessary

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Fingerprint Image Enhancement

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  1. Fingerprint Image Enhancement Joshua Xavier Munoz-Ramos

  2. Motivation • Method for fingerprint image enhancement • Ridge structure in fingerprint images are not always well defined; therefore, enhancement algorithm, is necessary • A critical step in automatic fingerprint matching is extracting minutiae from the input fingerprint images. However, the performance of a minutiae extraction relies on the quality of the images.

  3. Background • Two Important ridge characteristics • Ridge ending • Ridge bifurcation

  4. Approach • Original Grayscale fingerprint image • Local histogram equalization • Wiener Filtering • Binarization and thinning • Morphological and further filtering (Anisotropic Filter) • Enhanced binary image • http://fvs.sourceforge.net/C27_icpr2000.pdf • Fingerprint Image Enhancement: • Algorithm and Performance Evaluation • Lin Hong, Student Member, IEEE, Yifei Wan, and Anil Jain, Fellow, IEEE

  5. Weiner filter • W(n1,n2)=u +(v^2-n^2)/(v^2) [I(n1,n2)-u] • 3x3 matrix • Binary Thresholding (if I(n1,n2) > local mean set to 1 other wise set to 0)

  6. Weiner filter / binary

  7. Morphing/ anisotropic filter • Connecting the ridges through orientation fields

  8. Anisotropic filter Instead of using local gradients as a means of controlling the anisotropism of filters, it uses both a local intensity orientation and an anisotropic measure to control the shape of the filter. K(x0,x) = exp{-[((x-x0) n )^ 2/sig(x0)^2 + ((x-x0) n(ortho))^2/sig(x0)^2 h(x0,x) = -2 +10*k(x0,x)

  9. Results • 71.1% percent FAR (using verification system ) but only tested two fingerprints with 10 different pics… 7/10 were identified • False ridges endings and bifurcations • Need to test more fingerprints • A good image has around 40 to 50 correct ridge endings and bifurcations • (different method is to apply a garbor filter ) • Fingerprint acceptance rate • (enhancement did not work as well as expected) • Picture was clearer to see after enhancement, and the filters did smooth out noise • However many false ridges and bifurcations • Many parts where the picture was not clear my enhancement did not work. • Future work…. Fix the orientation field and the anisotropic filter.. Many details were lost. • (citation) http://fvs.sourceforge.net/C27_icpr2000.pdf

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