• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.57-64
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• 2009

In this paper, We propose an highly efficient preprocessing technique for improving the performance of the crease extraction method, which can improve the accuracy of feature extraction within the fingerprint image. The proposed method applies the 1-dimensional directional slit for each pixel in fingerprint image. Once the direction of every pixel in crease candidate area is estimated, it is decomposed into different images depending on their direction. From the directional images, the crease clusters are estimated by utilizing the property of crease area. The proposed method finally extracts the crease from the crease clusters estimated from directional images.

• 전자공학회논문지 IE
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• v.44 no.2
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• pp.32-40
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• 2007

In this paper, the crease extraction method is proposed to improve the accuracy of feature extraction within the fingerprint image. First of all, for each pixel in fingerprint image, it calculates the average grey level and variance to determine if the current pixel composes the crease, and estimates the direction of crease. Secondly, once the direction of every pixel in crease candidate area is estimated, it is decomposed into 8 different images, depending on their direction. The properties of crease consists of the length of the crease candidate area, the correspondence between the crease direction and the pixel distribution direction, the difference between the ridge direction and the pixel distribution direction, and finally the grey level of the candidate pixels. The proposed method finally extracts the crease from the crease clusters estimated from directional images. In conclusion, applying the proposed method improved the accuracy of overall feature extraction by 91.4% by accurately and precisely extracting the crease from fingerprint image.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.90-97
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• 2007

For a highly reliable fingerprint recognition system, the precise and accurate feature extraction is indispensable. In this paper, We propose a highly efficient crease extraction method, which can improve the accuracy of feature extraction within the fingerprint image. The proposed method applies the 1-dimensional directional slit for each pixel in fingerprint image. And then it calculates the average grey level and variance to determine whether the current pixel composes the crease, and estimates the direction of crease. Once the direction of every pixel in crease candidate area is estimated, it is decomposed into 8 different images depending on their direction. From the 8 directional images, the crease clusters are estimated by utilizing the property of crease area. The proposed method finally extracts the crease from the crease clusters estimated from directional images. In conclusion, the proposed method highly improved the accuracy of overall feature extraction by accurate and precise extraction of the crease from fingerprint image.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.818-820
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• 1999

In this paper, a new boundary tracking algorithm for extracting finger area, which may be utilized by a finger crease pattern recognition algorithm, is proposed. Due to noise and irregular illumination, conventional algorithms for boundary tracking such as skeleton-based tracking methods were not suitable for typical boundary image of hand. So we propose a new finger boundary tracking algorithm utilizing a boundary-point-detection mask. We have observed that the proposed method provides stable and optimised boundary tracking.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.35-46
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• 1994

In this paper, the performance of DS/SS acquisition system over frequency nonselective Rician fading channel is analyzed by means of packet loss probability. The power ratio of the fading component to the specular compnent. seccessive constant fadong chips k. and correlation coefficient among k chipe are taken for channel parameters. The false alarm probabilities and detection probabilities are derived, and packet loss probability is evaluated in terms of these probablities in the stats transition diagram. From the results of the performance test, these exists the region of packet loss probability in crease because of autocorrelation sidelobe. As k increases, the packet loss probabolotoes decrease.