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首页> 外文期刊>IEEE Computer Graphics and Applications >Extracting Valley-Ridge Lines from Point-Cloud-Based 3D Fingerprint Models
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Extracting Valley-Ridge Lines from Point-Cloud-Based 3D Fingerprint Models

机译：从基于点云的3D指纹模型中提取山谷脊线

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摘要

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.
机译：3D指纹技术是一种新兴技术，具有非接触式操作的独特优势。更重要的是，3D指纹模型比传统的2D指纹图像包含更多的生物特征信息。但是，当前的指纹特征检测方法通常必须通过展开或其他方法将3D模型转换为2D空间，这可能会引入失真。一种新方法可直接从基于点云的3D指纹模型中提取出山脊特征。它首先应用移动最小二乘法来拟合局部抛物面，并表示局部点云区域。然后，它计算局部表面的曲率和曲率张量，以方便检测潜在的谷点和山脊点。该方法使用协方差分析和互相关等统计手段将这些点投影到最可能的谷脊线。为了最终提取出谷脊线，它会增加逼近投影特征点的折线并消除采样点之间的干扰。使用不同的3D指纹模型进行的实验证明了该方法的可行性和性能。

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