Development of Automatic Fingerprint Verification Systems
Date Issued
2004
Date
2004
Author(s)
Chen, Po-Jui
DOI
en-US
Abstract
Most fingerprint verification systems take advantage of fingerprint minutiae as matching features. Since the classification rate of the minutia-based method is determined by the quality of input fingerprint images, the minutia-based method usually demands a large amount of image preprocessing to remove signal noise. Obviously, this not only increases system computation complexity, but also reduces matching speed. In order to avoid these drawbacks, this thesis combines the automatic threshold selection with differential pulse transform algorithm and adopts the wavelet transform to transfer a fingerprint signal from the spatial domain to the frequency domain. The magnitudes of the fingerprint energies, which distribute over different frequencies, are taken as fingerprint features for identification or verification, to reduce the computation load in the system.
As for low quality fingerprint images, we join the wavelet transform and Gabor filter to enhance and restore crumbling segments. In addition, we use the registration point and rotation point as auxiliary reference to solve for the problem of frequency characteristic variations induced by fingerprint translation or rotation. With back propagation neural network as classifier, experiment shows that the classification rate can achieve 93% above in our system. The same result can be obtained for the fingerprint images in varied position or with poor quality.
Subjects
指紋辨識
指紋強化
脈波差值轉換
小波轉換
蓋伯函數
fingerprint verification
fingerprint enhancement
Gabor filter
Differential pulse transform
wavelet transform
Type
thesis
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