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Issue No.04 - July-Aug. (2013 vol.33)
pp: 73-81
Xufang Pang , Shenzhen Inst. of Adv. Technol., Shenzhen, China
Zhan Song , Shenzhen Inst. of Adv. Technol., Shenzhen, China
Wuyuan Xie , Chinese Univ. of Hong Hong, Hong Kong, China
ABSTRACT
3D fingerprinting is an emerging technology with the distinct advantage of touchless operation. More important, 3D fingerprint models contain more biometric information than traditional 2D fingerprint images. However, current approaches to fingerprint feature detection usually must transform the 3D models to a 2D space through unwrapping or other methods, which might introduce distortions. A new approach directly extracts valley-ridge features from point-cloud-based 3D fingerprint models. It first applies the moving least-squares method to fit a local paraboloid surface and represent the local point cloud area. It then computes the local surface's curvatures and curvature tensors to facilitate detection of the potential valley and ridge points. The approach projects those points to the most likely valley-ridge lines, using statistical means such as covariance analysis and cross correlation. To finally extract the valley-ridge lines, it grows the polylines that approximate the projected feature points and removes the perturbations between the sampled points. Experiments with different 3D fingerprint models demonstrate this approach's feasibility and performance.
INDEX TERMS
Fingerprint recognition, Solid modeling, Feature extraction, Surface fitting, Computational modeling, Cameras, Three dimensional displays,curvatures, Fingerprint recognition, Solid modeling, Feature extraction, Surface fitting, Fingers, Computational modeling, Cameras, computer graphics, 3D fingerprints, feature detection, fingerprint detection, valley-ridge lines
CITATION
Xufang Pang, Zhan Song, Wuyuan Xie, "Extracting Valley-Ridge Lines from Point-Cloud-Based 3D Fingerprint Models", IEEE Computer Graphics and Applications, vol.33, no. 4, pp. 73-81, July-Aug. 2013, doi:10.1109/MCG.2012.128
REFERENCES
1. A.K. Jain, A. Ross, and S. Pankanti, “Biometrics: A Tool for Information Security,” IEEE Trans. Information Forensics and Security, vol. 1, no. 2, 2006 pp. 125-143.
2. R. Cappelli et al., “Performance Evaluation of Fingerprint Verification Systems,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 1, 2006 pp. 3-18.
3. J.C. Wu and C.L. Wilson, “Nonparametric Analysis of Fingerprint Data on Large Data Sets,” Pattern Recognition, vol. 40, no. 9, 2007 pp. 2574-2584.
4. S.M. Rao, “Method for Producing Correct Fingerprints,” Applied Optics, vol. 47, no. 1, 2008 pp. 25-29.
5. A. Fatehpuria et al., “Performance Analysis of Three-Dimensional Ridge Acquisition from Live Finger and Palm Surface Scans,” Biometric Technology for Human Identification IV, Proc. SPIE, vol. 6539, SPIE, 2007 pp. 1-12.
6. M. Alexa et al., “Computing and Rendering Point Set Surfaces,” IEEE Trans. Visualization and Computer Graphics, vol. 9, no. 1, 2003 pp. 3-15.
7. W.Y. Xie, Z. Song, and X.T. Zhang, “A Novel Photometric Method for Real-Time 3D Reconstruction of Fingerprint,” Advances in Visual Computing, LNCS 6454, Springer, 2010 pp. 31-40.
8. P. Hanrahan and W. Krueger, “Reflection from Layered Surfaces due to Subsurface Scattering,” Proc. Siggraph, ACM, 1993 pp. 165-174.
9. M. Meyer et al., “Discrete Differential-Geometry Operators for Triangulated 2-Manifolds,” Visualization and Mathematics III, Springer, 2003 pp. 35-57.
10. L. Hesselink, Y. Levy, and Y. Lavin, “The Topology of Symmetric, Second-Order 3D Tensor Fields,” IEEE Trans. Visualization and Computer Graphics, vol. 3, no. 1, 1997 pp. 1-11.
11. J. Daniels II et al., “Robust Smooth Feature Extraction from Point Clouds,” Proc. 2007 IEEE Int'l Conf. Shape Modeling and Applications (SMI 07), IEEE, 2007 pp. 123-136.
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