Fingerprint-Quality Index Using Gradient Components
Fingerprint-Quality Index Using Gradient Components
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DOI：
10.1109/tifs.2008.2007245
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发表时间：
2008-12
影响因子：
6.8
通讯作者：
Sanghoon Lee;Heeseung Choi;Kyoungtaek Choi;Jaihie Kim
中科院分区：
文献类型：
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作者：
Sanghoon Lee;Heeseung Choi;Kyoungtaek Choi;Jaihie Kim
指纹图像质量检测是指纹识别中最重要的问题之一，因为指纹识别在很大程度上受指纹图像质量的影响。在过去，许多相关的指纹质量检查方法通常都会考虑输入图像的情况。然而，在使用预处理算法时，有时可能会错误地提取脊线方向。可能会产生不想要的虚假细节点或忽略一些真实的细节点，这也会直接影响识别性能。因此，在本文中，我们提出了一种新的质量检测算法，该算法考虑了输入指纹和方位估计误差的情况。在实验中，首先将指纹图像的二维梯度分离为两组一维梯度。然后，测量这些梯度的概率密度函数的形状，以确定指纹质量。我们使用FVC2002数据库和合成指纹图像从三个方面对该方法进行了评估：1)质量估计能力；2)好坏区域可分性；3)验证性能。实验结果表明，根据质量退化程度，该方法得到了一个合理的质量指标。实验结果表明，该方法在可分离性和验证性能方面均优于已有方法。
Fingerprint image-quality checking is one of the most important issues in fingerprint recognition because recognition is largely affected by the quality of fingerprint images. In the past, many related fingerprint-quality checking methods have typically considered the condition of input images. However, when using the preprocessing algorithm, ridge orientation may sometimes be extracted incorrectly. Unwanted false minutiae can be generated or some true minutiae may be ignored, which can also affect recognition performance directly. Therefore, in this paper, we propose a novel quality-checking algorithm which considers the condition of the input fingerprints and orientation estimation errors. In the experiments, the 2-D gradients of the fingerprint images were first separated into two sets of 1-D gradients. Then, the shapes of the probability density functions of these gradients were measured in order to determine fingerprint quality. We used the FVC2002 database and synthetic fingerprint images to evaluate the proposed method in three ways: 1) estimation ability of quality; 2) separability between good and bad regions; and 3) verification performance. Experimental results showed that the proposed method yielded a reasonable quality index in terms of the degree of quality degradation. Also, the proposed method proved superior to existing methods in terms of separability and verification performance.