By Pushpita Biswas

By Pushpita Biswas

Palm print Verification for Controlling Access to Shared Computing Resources

Under the guidance of Prof. S.Mukhopadhyay and Prof. P.K.Biswas

Why access security is used?

Why Palm print verification?

1. no need to memorize codes or passwords.

2. more reliable

Four Stages of Palm print Verification

Image acquisitionPalm positioning Feature extraction Palm print matching

Image acquisition

Palm Positioning

Feature extraction

Register or

verify?

Palm print matching

TIFF file (gray scale)

Gray-scale Image

Line edge map

Verify

Decision

Register

Registered model

Database

Flow Chart

1. 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

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 referenceRatio_Gradient => predetermined constant between 0 and 1 T_Gradient => Threshold value

T_Gradient = Gr * Ratio_Gradient3. Selected pixels removed from binary image to reduce all lines

in the image to a single pixel width.

Feature point locationIn 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

The x-axis passes through K1 and K3.The y-axis is perpendicular to the x-axis and passes through K2

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 parallel pairs)

Sub image normalization

The rectangle specifications :1.distance between x-axis and

rectangle’s nearest side isRefLength * 0.25,

RefLength =>distance between K1 and K3

2.sides parallel to x-axis and y-axis3.symmetric with respect to y-axis4.sides have length of RefLength

Scaling and rotation is followed

3. Feature extraction

Image PreprocessingA 3*3 averaging mask is used, which smoothes the image and minimizes the noise impact.

Line DetectionStandard Sobel edge detector is used followed by thresholding on edge magnitude.

Image ThresholdingThreshold value calculated on basis of a percentage of image area.

Line thinningResulting image contains lines of only a single pixel width

Results

Results

Next

Thresholding of two sample images, of same person captured under different

lighting conditions

Return

Result of line detection

Return

Thinning and straight line approximation

Result of thinning Result of 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.

4. Palm print matching1. 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

2. Decision Making

Choice of method depends on system specification

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

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.

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.

Thank you