Issue No. 04 - July-Aug. (2013 vol. 33)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MCG.2012.128
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
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.
Fingerprint recognition, Solid modeling, Feature extraction, Surface fitting, Computational modeling, Cameras, Three dimensional displays
Xufang Pang, Zhan Song and Wuyuan Xie, "Extracting Valley-Ridge Lines from Point-Cloud-Based 3D Fingerprint Models," in IEEE Computer Graphics and Applications, vol. 33, no. 4, pp. 73-81, 2013.