评估低质量图像和图像增强方法的指纹验证

Hideyo Takeuchi, T. Umezaki, N. Matsumoto, K. Hirabayashi
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引用次数: 8

摘要

市面上随处可见的指纹验证设备存在的一个严重问题是如何处理“干手指”,这使得指纹读取困难,往往导致验证错误。手指干燥在老年人和经常从事湿活的家庭主妇中很常见。在这些情况下,观察到的指纹往往成为侵蚀指纹图像,具有断开的脊。提出了一种自动识别可能导致验证错误的侵蚀指纹图像并快速恢复其纹路的方法。首先,讨论了一种基于噪声评价的指纹图像质量评价方法,并验证了该方法的有效性。然后,对自动判断为侵蚀指纹的图像,利用边缘强调进行简单的脊恢复;在实验中,指纹数据库是在冬季采集的,在冬季手指干燥是常见的。当对其他人的拒绝率设定为99.99%时,对合适人选的接受率(识别率)为94.8%。当采用本文提出的图像评价方法对质量较低的指纹图像进行纹线恢复时,识别率提高到96.2%。在考虑干手指容忍度,对判定为难以恢复的低质量图像不进行验证的情况下,识别率提高到97.2%。在很少用户干手指的环境下,当对其他人的拒绝率设置为99.99%时,识别率将提高到98.8%。©2007 Wiley期刊公司电子工程学报,2009,35 (3):393 - 393;在线发表于Wiley InterScience (www.interscience.wiley.com)。DOI 10.1002 / ecjc.20345
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Evaluation of low‐quality images and imaging enhancement methods for fingerprint verification
A serious problem of fingerprint verification devices which are easily available on the market is how to handle ‘‘dry fingers,’’ which make the reading of fingerprints difficult and often result in incorrect verification. Dry fingers are frequently observed in the elderly and in housewives who are frequently engaged in wet work. In these cases, the observed fingerprint tends to become an eroded fingerprint image, with disconnected ridges. This paper proposes a method of handling eroded fingerprint images in which such images that will result in incorrect verification are automatically identified and the ridges are recovered rapidly. First, a method for image quality evaluation that consists of evaluating the noise in the fingerprint image is discussed and the effectiveness of the method is demonstrated. Then, simple ridge recovery is performed by using edge emphasis for images which are automatically judged to be eroded fingerprints. In the experiment, a fingerprint database was acquired in winter, when dry fingers are common. The acceptance rate for the right person (recognition rate) is found to be 94.8% when the rejection rate for other persons is set as 99.99%. When the ridges are recovered in fingerprint images whose quality is judged to be low by the image evaluation measure proposed in this paper, the recognition rate is improved to 96.2%. Furthermore, when tolerance to dry fingers is considered and verification is not performed for low-quality image judged to be difficult to recover, the recognition rate is improved to 97.2%. In environments in which few users have dry fingers, the recognition rate will be improved to 98.8% when the rejection rate for other persons is set as 99.99%. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(10): 40– 53, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20345
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