• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.963-966
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• 1998

In this paper, a fingerprint classification algorithm is presented. Fingerprint types are classified into five categories: arch, tented arch, left loop, right loop and whorl. Singular points (cores and deltas) are detected using Poincare index on the directional image smoothed by adaptive window size. The method is shown to be robust to the variation of fingerprint image qualaity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2529-2531
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• 2009

A simple multimodal fingerprint recognition method based on two types of feature vectors such as minutiae points and directional features is proposed, where Directional Filter Bank (DFB) is used to extract directional features. Experimental results show that the proposed method can effectively combine minutiae- and DFB-based methods and produce a better matching capability in the poor quality fingerprint image.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.19-28
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• 2007

The purpose of this paper is to present a new measure for fingerprint image quality assessment that has a considerable effect on evaluation of fingerprint databases. This paper introduces a hybrid segmentation method for measuring an image quality and evaluates the experimental results using various fingerprint databases. This study compares the performance of the proposed hybrid segmentation using variance and coherence of fingerprints against the NIST's NFIQ program. Although NFIQ is a most widely used tool, it classifies the image quality into 5 levels. However, the proposed hybrid method is developed to be conformant to the ISO standards and accordant to human visual perception. The experimental results demonstrate that the hybrid method is able to produce finer quality measures.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1005-1010
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• 2012

We propose a novel approach to classify fingerprints using the extracted adaptive core block for improving classification performance of incomplete fingerprints in this paper. We compute representative directions from fingerprint images by the block unit and learn horizontal and vertical Markov models by deciding the center position of a fingerprint image based on the expert knowledge. The center block of a test image is the block has the highest probability after comparing the Markov model with $11{\times}11$ blocks. The proposed approach can effectively classify incomplete fingerprints using the optimal center block.

• The Journal of the Korea Contents Association
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• v.2 no.4
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• pp.46-50
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• 2002

This paper suggests the adaptive binary method which applies advanced silt sum technique, so that threshold value can be changed heuristically according to the brightness of captured fingerprint image. Through this research, we tried to resolve threshold value setting issue by the local differences of brightness of fingerprint image in the binary image preprocessing. The experimental results show that our proposed preprocessing method demonstrates the better recognition accuracy and can be applied to minutiae extraction algorithm for fingerprint recognition system.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.321-324
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• 2002

In this paper, we introduce a new approach to fingerprint classification based on both singular points and gabor features. We find singular points of fingerprint image by using squared direction field and Poincare index. Then, the input fingerprint image can be classified into one of 5 classes using the number of singular points and their location. However, it is often impossible to classify the fingerprint image because the numbers and the position of the singular points are not correct due to noise. In this case Gabor features are extracted from unclassified images using Gator filter and they are classified by using k-NN classifier. This method has been tested on the NIST-4 database. The experimental results show that the proposed method is reliable.

• Journal of the Korea Computer Industry Society
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• v.3 no.12
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• pp.1775-1784
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• 2002

Fingerprint recognition technology is used in many biometrics field accordingly essential feature of fingerprint image and the study is progressing. However development is not perfect in performance of the fingerprint recognition and application of the usual life. In the paper, we study various necessity of preprocessing according to algorithm and circumstances of authentication system in automatic information machine. We prove that system circumstance and optation of fingerprints image effectively is the important factor by using optical fingerprint input device and scanning the fingerprint in ID card. And then we present correct and fast computation method for improving image and feature extraction of fingerprint. Also we study effective algorithm implementation of total system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.6
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• pp.1482-1489
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• 1998

This paper aims to extract characteristics of the spectrum of fingerprint image and to apply them to image enhancement techniques in spatial frequency domain. Based on 1$\times$64 window as a processing unit and the different record lengths(32, 16, 8), the estimate of power spectrum density for each length was made. Each acquired spectrum characteristics was applied to the re-synthesis process of the fingerprint image, an improved gray scale image was obtained. In order to select an optimal predictor and the Huffman table for the fingerprint iamge, the lossless JPEG algorithm was used. Experiments were performed for extracting distribution characteristics for the each of 7 predictors from the fingerprint image and modeling processes, and the result was applied to the data compression algorithm and the selection of the optimal predictor.

• Proceedings of the IEEK Conference
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• 2001.06b
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• pp.75-78
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• 2001

In this paper, we proposed a personal identification method using binay image encryption technique and decryption system in JTC structure. Logo which represents the group symbol was encrypted with personal fingerprint and JTC structure decrypts this logo. The logo can not decrypted by other unused fingerprint even if the encrypted image was lost or stolen. So this method can give more safe personal identification.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1337-1347
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• 2010

Fingerprint recognition is a biometric technology to identify individual by using fingerprint features such ridges and valleys. Most fingerprint systems perform the recognition based on minutiae points after acquiring a fingerprint image from contact type sensor. They have an advantage of acquiring a clear image of uniform size by touching finger on the sensor. However, they have the problems of the image quality can be reduced in case of severely dry or wet finger due to the variations of touching pressure and latent fingerprint on the sensor. To solve these problems, the contactless capturing devices for a fingerprint image was introduced in previous works. However, the accuracy of detecting minutiae points and recognition performance are reduced due to the degradation of image quality by the illumination variation. So, this paper proposes a new LDP-based fingerprint recognition method. It can effectively extract fingerprint patterns of iterative ridges and valleys. After producing histograms of the binary codes which are extracted by the LDP method, chi square distance between the enrolled and input feature histograms is calculated. The calculated chi square distance is used as the score of fingerprint recognition. As the experimental results, the EER of the proposed approach is reduced by 0.521% in comparison with that of the previous LBP-based fingerprint recognition approach.