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Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection

机译:通过自动选择刻度的比例空间运算符的形状适应来增强指纹

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This work presents two mechanisms for processing fingerprint images; shape-adapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives. The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without destroying essential singularities such as branching points and enforces continuity of their directional fields. The scale selection procedure estimates local ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is used for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. The result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed grey-level version of the input image. We propose that these general techniques should be of interest to developers of automatic fingerprint identification systems as well as in other applications of processing related types of imagery.
机译:这项工作提出了两种处理指纹图像的机制。基于第二矩描述符的形状自适应平滑和基于归一化导数的自动缩放选择。形状适应过程使平滑操作适应于局部脊结构,这允许在不破坏本质奇异点(例如分支点)的情况下连接不连续的脊并增强其方向场的连续性。音阶选择过程估计局部脊的宽度,并使平滑量适应于局部噪声量。另外,定义了一个脊度度量,该度量反映了局部图像结构与定性脊模型的吻合程度,并用于将形状适应的结果扩展到嘈杂的区域。组合的方法使解析清晰区域中的精细结构成为可能,同时降低了在模糊或破碎的区域中增强噪声的风险。结果是对脊取向场和脊宽度以及输入图像的平滑灰度版本进行了可靠且自适应的详细估计。我们建议自动指纹识别系统的开发人员以及处理相关类型图像的其他应用程序应该对这些通用技术感兴趣。

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