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首页> 外文会议>International Conference on Advances in Biometrics(ICB 2007); 20070827-29; Seoul(KR) >Robust Point-Based Feature Fingerprint Segmentation Algorithm
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Robust Point-Based Feature Fingerprint Segmentation Algorithm

机译：基于鲁棒点的特征指纹分割算法

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摘要

A critical step in automatic fingerprint recognition is the accurate segmentation of fingerprint images. The objective of fingerprint segmentation is to decide which part of the images belongs to the foreground containing features for recognition and identification, and which part to the background with the noisy area around the boundary of the image. Unsupervised algorithms extract blockwise features. Supervised method usually first extracts point features like coherence, average gray level, variance and Gabor response, then a Fisher linear classifier is chosen for classification. This method provides accurate results, but its computational complexity is higher than most of unsupervised methods. This paper proposes using Harris corner point features to discriminate foreground and background. Shifting a window in any direction around the corner should give a large change in intensity. We observed that the strength of Harris point in the foreground area is much higher than that of Harris point in background area. The underlying mechanism for this segmentation method is that boundary ridge endings are inherently stronger Harris corner points. Some Harris points in noisy blobs might have higher strength, but it can be filtered as outliers using corresponding Gabor response. The experimental results proved the efficiency and accuracy of new method are markedly higher than those of previously described methods.
机译：自动指纹识别中的关键步骤是指纹图像的精确分割。指纹分割的目的是确定图像的哪一部分属于包含用于识别和识别的特征的前景，以及哪一部分属于具有围绕图像边界的噪声区域的背景。无监督算法提取块状特征。监督方法通常首先提取诸如相干性，平均灰度，方差和Gabor响应之类的点特征，然后选择Fisher线性分类器进行分类。该方法可提供准确的结果，但其计算复杂度高于大多数无监督方法。本文提出使用哈里斯角点特征来区分前景和背景。在拐角处沿任何方向移动窗口都应使强度发生很大变化。我们观察到前景区域中的哈里斯点的强度远高于背景区域中的哈里斯点的强度。这种分割方法的潜在机制是边界脊末端固有地具有更强的Harris角点。嘈杂斑点中的某些Harris点可能强度更高，但可以使用相应的Gabor响应将其过滤为离群值。实验结果证明，新方法的效率和准确性明显高于先前描述的方法。

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