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Extracting Valley-Ridge Lines from Point-Cloud-Based 3D Fingerprint Models
July-Aug. 2013 (vol. 33 no. 4)
pp. 73-81
Xufang Pang, Shenzhen Institutes of Advanced Technology
Zhan Song, Shenzhen Institutes of Advanced Technology
Wuyuan Xie, Chinese University of Hong Hong
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
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