• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.323-330
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• 2008

In this paper, we propose a new fingerprint sensing method that can reduce orientation error. General fingerprint input methods need finger to be put on the surface of a sensor. It can cause of rotation problem and it affects the recognition result significantly. This improved input method can minimize the rotation of a finger by holding double-sided sensor with both thumb and index finger at the same time. Whenever fingerprint is impressed, it has nearly the same orientation because sensors are located between two fingers. As a result, we can get a better performance in fingerprint recognition system, but it may need more hardware cost.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.61-68
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• 2002

This paper presents a new matching algorithm for fingerprint recognition, which is robust to image translation. The basic idea of this paper is to estimate the translation vector of an imput fingerprint image using N minutiae at which the gradient of the ridge direction field is large. Using the estimated translation vector we select minutiae irrelevant to the translation. We experimentally prove that the presented algorithm results in good performance even if there are large translation and pseudo-minutiae.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.359-361
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• 2008

This paper presents the fuzzy minutiae-based matching to improve the accuracy of the difference between template and imput fingerprint image. Minutiae-based matching method is the most well-known and widely used method for fingerprint matching. However, fingerprint pressure, dryness of the skin, skin disease, sweat, dirt, grease, and humidity in the air cause the noisy fingerprint images and the distortion is produced by users moving their fingers on the scanner surface. The input image may be rejected from the Fingerprint Recognition System, because the distorted fingerprint image is very different from the original image. Large tolerence boxes and fuzzy discriminant function is required to improve the accuracy.