Automated Fingertip Detection

Thesis Defense Presentation by: Joseph Butler

Outline• Introduction • Related Work• Our solution

o Color and texture maskingo Auto-rotationo Orientation estimationo Poincare indexo Support vector classificationo Connected neighbors and automated cropping

• Results• Conclusion

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

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

Color Mask

• Difference between two curves• Generate binary mask

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

k’=(k+1)mod(N)

Poincare Index• A measure of the difference

between a block’s orientation value and those of its neighbors

Delta Delta

Core

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

• Classify test image blocks as core or non-core

• Differentiate erroneous classifications

• True core blocks found in groups

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

• 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.