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By Pushpita Biswas

Palm print Verification for Controlling Access to Shared Computing Resources. By Pushpita Biswas. Under the guidance of Prof. S.Mukhopadhyay and Prof. P.K.Biswas. Why access security is used?. 1. no need to memorize codes or passwords. 2. more reliable.

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By Pushpita Biswas

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  1. Palm print Verification for Controlling Access to Shared Computing Resources By Pushpita Biswas Under the guidance of Prof. S.Mukhopadhyay and Prof. P.K.Biswas

  2. Why access security is used? 1. no need to memorize codes or passwords.2. more reliable Why Palm print verification?

  3. FourStagesof Palm print Verification • Image acquisition • Palm positioning • Feature extraction • Palm print matching

  4. Image acquisition Flow Chart TIFF file (gray scale) Palm Positioning Gray-scale Image Feature extraction Line edge map Register Register or verify? Registered model Verify Palm print matching Database Decision

  5. Image acquisition • Image of the user’s hand is taken via a camera and stored a grayscale TIFF file. • 2. Palm positioning • Boundary extraction and edge thinning • Feature point location • Establishment of coordinate system • Sub image normalization

  6. Boundary extraction and edge thinning 1. Gradient magnitude of each pixel computed using set of sobel masks for detecting horizontal, vertical and diagonal edges. 2. Adaptive thresholding :- Gr => highest gradient value taken as reference Ratio_Gradient => predetermined constant between 0 and 1 T_Gradient => Threshold value T_Gradient = Gr * Ratio_Gradient 3. Selected pixels removed from binary image to reduce all lines in the image to a single pixel width.

  7. Feature point location In the boundary image’s line pattern the bottom of a valley is a short curve joining the edges of adjacent fingers. The key points are best represented as those curve’s midpoints. Establishment of the coordinate system 1. Sort the parallel line pairs, so that the line pairs are stored in left to right order. 2. For each parallel pair Pi in the sorted array, form a V- shape pair with the right edge of Pi and the left edge of Pi+1 (i = 0..I-2, where I is the total number of parallelpairs) The x-axis passes through K1 and K3.The y-axis is perpendicular to the x-axis and passes through K2

  8. Sub image normalization The rectangle specifications : 1.distance between x-axis and rectangle’s nearest side is RefLength * 0.25, RefLength =>distance between K1 and K3 2.sides parallel to x-axis and y-axis 3.symmetric with respect to y-axis 4.sides have length of RefLength Scaling and rotation is followed

  9. 3. Feature extraction • Image Preprocessing A 3*3 averaging mask is used, which smoothes the image and minimizes the noise impact. • Line Detection Standard Sobel edge detector is used followed by thresholding on edge magnitude. • Image Thresholding Threshold value calculated on basis of a percentage of image area. • Line thinning Resulting image contains lines of only a single pixel width Results Results Next

  10. Thresholding of two sample images, of same person captured under different lighting conditions Return

  11. Result of line detection Return

  12. Thinning and straight line approximation Contour tracing and the Dynamic Two-Strip (DYN2S) algorithm is applied to establish a set of straight line segments that approximate the extracted palm print lines. Result of thinning Result of Line approximation

  13. 4. Palm print matching 1. Line segment Hausdorff distance (LHD) is applied. m and t are 2 line segments Angle distance by tangent function with respect to smallest angle between m and t. Predetermined weight of angle distance

  14. 2. Decision Making Choice of method depends on system specification

  15. Results for palm print matching system. Thus Threshold value is decided.

  16. Conclusion The system will work well on images with a uniform background, but this can be further extended to handle images with arbitrary backgrounds. Since the algorithm for locating and aligning the palm print is based on line detection instead of simple segmentation, makes the system more robust and suitable for security applications with outdoor cameras.

  17. References • M.K.Leung, A.C.M. Fong, Siu Cheung Hui “Palm print Verification for Controlling Access to Shared Computing Resources,” IEEE Pervasive Computing, vol. 6, no. 4, 2007, pp. 40–47. • W.J. Rucklidge, “Efficiently Locating Objects Using the Hausdorff Distance,” Int’l J. Computer Vision, vol. 24, no. 3, 1997,pp. 251–270.   • M.K. Leung and Y.H. Yang, “Dynamic Two-Strip Algorithm in Curve Fitting,” Pattern Recognition, vol. 23, nos. 1–2, 1990, pp. 69–79.

  18. Thank you

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