1. Automated Fingertip Detection
Formally thank my advisor Dr. Egbert and my committee members Dr. Morse and Dr. Ringger
Thesis Defense Presentation by: Joseph Butler

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

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
Constant work is being done to make the modality of fingerprint identification more effective. It is necessary not only to approve matching techniques but also to improve capture techniques so that the quality of the sample and the efficiency of the matching are as good as possible.

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 Wang also 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
Steps for optimal threshold:
Find probabilities of a pixel falling into either curve
Find mean and standard deviation of each curve
Solve for T taking the value between the two means

6. Color Mask Difference between two curves Generate binary mask
Steps for optimal threshold:
Find probabilities of a pixel falling into either curve
Find mean and standard deviation of each curve
Solve for T taking the value between the two means

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
𝑃𝑜𝑖𝑛𝑐𝑎𝑟𝑒 𝑖,𝑗 = 1 2𝜋 𝑘=0 𝑁−1 ∆ 𝑘
∆𝑘= 𝛿(𝑘) 𝛿 𝑘 < 𝜋 2 𝜋+𝛿(𝑘) 𝛿 𝑘 < −𝜋 2 𝜋−𝛿(𝑘) 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
𝛿 𝑘 =𝜃 𝑋 𝑘 ′ ,𝑌 𝑘 ′ −𝜃 𝑋 𝑘 ,𝑌 𝑘
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
Support Vector Classification

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

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
Results

17. Example from web collection

18. Good Example from our collection

19. Bad Example 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, Software available at
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 , 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 , 2006.
S. Wang and Y. Wang. Fingerprint enhancement in the singular point area. IEEE Signal Processing Letters, 11(1): , 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.