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

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

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.

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

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

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

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

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

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

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

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)

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

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

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

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

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

Example from web collection

Good Example from our collection

Bad Example from our collection

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

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.