Fingerprint image enhancement: algorithm and performance evaluation

Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI
IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 5.69). 09/1998; DOI: 10.1109/34.709565
Source: IEEE Xplore

ABSTRACT In order to ensure that the performance of an automatic
fingerprint identification/verification system will be robust with
respect to the quality of input fingerprint images, it is essential to
incorporate a fingerprint enhancement algorithm in the minutiae
extraction module. We present a fast fingerprint enhancement algorithm,
which can adaptively improve the clarity of ridge and valley structures
of input fingerprint images based on the estimated local ridge
orientation and frequency. We have evaluated the performance of the
image enhancement algorithm using the goodness index of the extracted
minutiae and the accuracy of an online fingerprint verification system.
Experimental results show that incorporating the enhancement algorithm
improves both the goodness index and the verification accuracy

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    ABSTRACT: This paper studies ways to detect good users for biomet-ric recognition based on keystroke dynamics. Keystroke dynamics is an active research field for the biometric scientific community. Despite the great efforts made during the last decades, the performance of keystroke dynamics recognition systems is far from the performance achieved by traditional hard biometrics. This is very pronounced for some users, who generate many recognition errors even with the most sophisticate recognition algorithms. On the other hand, previous works have demonstrated that some other users behave particularly well even with the simplest recognition algorithms. Our purpose here is to study ways to distinguish such classes of users using only the genuine enrollment data. The experiments comprise a public database and two popular recognition algorithms. The results show the effectiveness of the Kullback-Leibler divergence as a quality measure to categorize users in comparison with other four statistical measures.
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    ABSTRACT: The fingerprint images of people always vary in quality. Fingerprint identification is one of the most popular biometric technologies and is used in criminal investigations, commercial applications and so on. The performance of a fingerprint image-matching algorithm depends heavily on the quality of the input fingerprint images. Fingerprint recognition is one of the basic tasks of the Automated Fingerprint Identification Service (AFIS) of the most famous police agencies. In this paper we introduce a special method called fine enhancement method to analyze the fingerprint images both in space and in frequency. This helps to eliminate the multispectral noise in the image, and then the image is filtered with median filter. From the filtered image we extract the minutiae. As a result more than 45 minutiae points are extracted. Further the orientation field is estimated with the specified angle. The spatial domain methods such as Contrast, Negative and Histogram image enhancement which operate directly on pixels are calculated. Experimental results show that our enhancement method improves the performance of the fingerprint Images and makes it more robust with respect to the quality of input compared to other methods.

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