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Automated Fingertip Detection

Automated Fingertip Detection. Thesis Defense Presentation by: Joseph Butler. Outline. Introduction Related Work Our solution Color and texture masking Auto-rotation Orientation estimation Poincare index Support v ector classification Connected neighbors and automated cropping

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Automated Fingertip Detection

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  1. Automated Fingertip Detection Thesis Defense Presentation by: Joseph Butler

  2. Outline • Introduction • Related Work • Our solution • Color and texture masking • Auto-rotation • Orientation estimation • Poincare index • Support vector classification • Connected neighbors and automated cropping • Results • Conclusion

  3. Introduction • Fingerprint modality one of the oldest biometric modalities • Extraction has gone from ink to touch sensors and now into digital images • Current work in digital image collection focuses on extraction • Complete automated system includes fingertip detection and extraction

  4. Related Work • Hong et al. use ridge orientation and frequency analysis to generate block specific Gabor filters to further enhance the contrast between friction ridges and valleys • Wang and Wangalso use ridge orientation estimation to calculate Poincare index values per block which are used to locate core and delta points • Lee et al. use a combination color and texture mask to isolate a single fingertip in a digital image • Hiew et al. captured digital images but used a highly controlled capture scenario which left the single preprocessing step of removing a set background color. Once captured the images were enhanced using a Short Time Fourier Transform

  5. Color Mask • Gathered skin-color samples from palms and/or fingers • Convert to Y’UV color space • Samples used to find distribution of U and V • Gaussian bimodal curve best fit for our distributions • Use optimal threshold technique to find threshold between curves

  6. Color Mask • Difference between two curves • Generate binary mask

  7. Texture Mask • Short depth of field given necessity to capture fine detail • Discrete wavelet transform • Two dimensional Haar wavelet • Binary mask • Combine color and texture mask

  8. Auto-rotation • Unrestrained capture • Leverage color mask • Find concentration of unmasked area • Rotate image so concentrated area is at the bottom

  9. Orientation Estimation • Use standard block size as a starting point • Find gradient in X direction and gradient in Y direction • Compute gradient average of entire block

  10. Orientation Estimation • Find ridge width using gradient average value • Resize blocks based on ridge width • Recalculate gradient average • Orthogonal to gradient is ridge orientation

  11. Poincare Index • Leverage orientation of each block k’=(k+1)mod(N)

  12. Poincare Index • A measure of the difference between a block’s orientation value and those of its neighbors Core Delta Delta Core & delta pair

  13. Support Vector Classification • Use training images to classify blocks as core or non-core • Create feature vectors using Poincare values of a block and its neighbors • Cast these feature vectors into a higher dimensional space find best fitting plane that divides the two classes

  14. Support Vector Classification • Classify test image blocks as core or non-core • Differentiate erroneous classifications • True core blocks found in groups

  15. Connected Neighbors and Automated Cropping • Recursively count number of connected neighbors • Identify core region

  16. Results • Our collection • Web collection • Number of fingertips that are identifiable • Positive detection rate • Expected versus actual

  17. Example from web collection

  18. Good Example from our collection

  19. BadExample from our collection

  20. Conclusion • Web collection had positive detection rate of 67.83% • Our collection had positive detection rate of 68.75% • Uncontrolled capture is difficult • Room for improvement • Future work

  21. References • C.C. Chang and C.J. Lin. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, pages 27:1{27:27, 2011. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm. • B.Y. Hiew, A.B.J. Teoh, and D.C.L. Ngo. Automatic digital camera based fingerprint image preprocessing. In Proceedings of the IEEE International Conference on Computer Graphics, Imaging and Visualization, pages 182-189, 2006. • C. Lee, S. Lee, J. Kim, and S.J. Kim. Preprocessing of a fingerprint image captured with a mobile camera. In Proceedings of International Conference on Advances in Biometrics, pages 348-355, 2006. • S. Wang and Y. Wang. Fingerprint enhancement in the singular point area. IEEE Signal Processing Letters, 11(1):16 - 19, 2004. • P. Yu, D. Xu, H. Li, and H. Zhou. Fingerprint image preprocessing based on whole-hand image captured by digital camera. In Proceedings of International Conference on Computational Intelligence and Software Engineering, pages 1-4, 2009.

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