• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.468-471
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• 2004

In this paper, a method for classifying fingerprint ridge lines using runlength codes is proposed. To detect feature points(minutiae) in automatic fingerprint identification system(AFIS), classification of fingerprint ridge lines are essential process. The fingerprint ridge lines are classified by run-length coding, and also the end and bifurcation regions in ridge lines are separated. To evaluate the performance of the proposed method, detected feature regions including minutiae points and classified fingerprint ridge lines are shown.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.41-49
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• 2008

Fingerprint classification is useful to reduce the matching time of a huge fingerprint identification system by categorizing fingerprints into predefined classes according to their global features. Although global features are distributed diversly because of the uniqueness of a fingerprint, previous fingerprint classification methods extract global features non-adaptively from the fixed region for every fingerprint. We propose an novel method that extracts features adaptively for each fingerprint in order to classify various fingerprints effectively. It extracts ridge directional values as feature vectors from the region after searching the feature region by calculating variations of ridge directions, and classifies them using support vector machines. Experimental results with NIST4 database show that we have achieved a classification accuracy of 90.3% for the five-class problem and 93.7% for the four-class problem, and proved the validity of the proposed adaptive method by comparison with non-adaptively extracted feature vectors.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.757-759
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• 2005

Henry의 지문분류법이 창안된 후, 지문분류에 대한 여러 가지 접근 방법이 연구되고 있다. 특이점에 의한 분류는 가장 많이 연구되고 있는 방법이지만, 지문영상의 품질에 민감하기 때문에 정확한 분류가 쉽지 않다. 의사 융선은 특이점과 더불어 지문을 분류하기 위한 특징으로, 특이점의 불완전함을 보완하는데 이용한다. 본 논문에서는 나이브 베이즈 분류기를 이용하여 특이점과 의사 융선 정보의 확률적인 분류 방법을 제안한다. NIST DB 4에 대해 제안하는 방법을 실험한 결과 5클래스 분류에 대해 $85.4\%$의 분류율을 획득하였으며, 제안하는 방법이 신경망, 최근접 이웃에 의한 분류에 비해 더 빠르다는 것을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.545-551
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• 2003

Henry System which is a traditional fingerprint classification model is difficult to apply to a modem Automatic Fingerprint Identification System (AFIS). To tackle this problem, this study is to apply algorithm for an An Ensemble Fingerprint Classroom System using changes of gradient of ridge in order to improve precise joining speed of a large volume of database. The existing classification system, Henry System, is useful in a captured fingerprint image of core point and delta point using paper and ink. However, the Henry System is unapplicable in modem Automatic Fingerprint Identification System (AFIS) because of problems such as size of input sensor and way of input. This study is to suggest an Ensemble Fingerprint Classroom System which can classify 5 basic patterns of Henry System in uncaptured delta image using changes of gradient of ridge. The proposed fingerprint classification technique will make an improvement of precise joining speed by reducing data volume.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.886-895
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• 2006

Fingerprint classification reduces the number of matches required in automated fingerprint identification systems by categorizing fingerprints into a predefined class. Support vector machines (SVMs), widely used in pattern classification, have produced a high accuracy rate when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with $na{\ddot{i}}ve$ Bayes classifiers. More specifically, it uses representative fingerprint features such as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and $na{\ddot{i}}ve$ Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for 5-class classification. Especially, it has effectively managed tie problems usually occurred in applying OVA SVMs to multi-class classification.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.33-38
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• 2005

지문분류는 대규모 인증시스템에 사용되는 지문 데이터 베이스를 종류별로 인덱싱 하거나 인식 시스템에 다양하게 쓰이는 매우 중요한 방법이다. 지문은 일반적으로 융선의 전체모양 등 전역적인 특징을 기반으로 분류하며, 분류방법에는 규칙기반 접근, 구문론적 접근, 구조적 접근, 통계적 접근, 신경망 기반 접근 등이 있다. 본 논문에서는 지문의 구조적인 특징을 바탕으로 관찰되는 특징의 상태가 매순간 변화하는 확률론적 정보추출 방식인 마코프 모델을 적용한 지문분류 방법을 제안한다. 지문 이미지의 전처리 과정을 거친 후 각 클래스 분류를 위해 대표 융선을 찾아 방향정보를 추출하고 이를 이용하여 5가지 클래스로 분류될 수 있도록 설계하였다. 좋은품질(Good)과 나쁜품질(Poor)의 데이터를 포함한 훈련집합을 사용하여 각 클래스별로 학습된 마코프 모델은 임의의 지문이미지 분류시 높은 분류율을 보였다. 또한 기존의 구조적 접근방법에 비하여 다양한 품질의 지문이미지의 방향성 정보를 이용한 확률론적 방법이기 때문에 예외적인 지문이미지 분류시 잘 적용될 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.262-264
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• 2006

지문분류는 지문을 전역특징에 따라 미리 정의된 클래스로 분류하는 기술로, 대규모 지문식별시스템의 매칭시간을 감소시키는데 유용하다. 지문은 개인마다 고유하기 때문에 각 지문마다 전역특징이 다양하게 분포하여 기존의 특징추출방법으로는 분류에 한계가 있다. 본 논문에서는 이를 해결하기 위하여 적응적 특징추출방법을 제안하였다. 이는 융선 방향의 변화량을 계산하여 지문의 전역특징을 포함하는 특징영역을 탐색한 뒤, 특징영역의 블록 방향성 정보로부터 특징벡터를 추출한다. NIST4 지문 데이터에 대한 5클래스 분류실험 결과 제안하는 특징추출방법이 90.25%의 분류성능을 보여 기존 방법보다 효과적임을 확인하였다.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.143-151
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• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

• The Journal of the Korea Contents Association
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• v.17 no.9
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• pp.132-144
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• 2017

The fingerprint classification of categorizing fingerprints by classes should be used in order to improve the processing speed and accuracy in a fingerprint recognition system using a large database. The fingerprint classification methods extract features from the fingerprint ridges of a fingerprint and classify the fingerprint using learning and reasoning techniques based on the classes defined according to the flow and shape of the fingerprint ridges. In earlier days, many researches have been conducted using NIST database acquired by pressing or rolling finger against a paper. However, as automated systems using live-scan scanners for fingerprint recognition have become popular, researches using fingerprint images obtained by live-scan scanners, such as fingerprint data provided by FVC, are increasing. And these days the methods of fingerprint classification using Deep Learning have proposed. In this paper, we investigate the trends of fingerprint classification technology and compare the classification performance of the technology. We desire to assist fingerprint classification research with increasing large fingerprint database in improving the performance by mentioning the necessity of fingerprint classification research with consideration for fingerprint images based on live-scan scanners and analyzing fingerprint classification using deep learning.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.870-878
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• 2013

In this paper, we propose a novel fingerprint classification method to enhance the accuracy and efficiency of the fingerprint identification system, one of biometrics systems. According to the previous researches, fingerprints can be categorized into the several patterns based on their pattern of ridges and valleys. After construction of fingerprint database based on their patters, fingerprint classification approach can help to accelerate the fingerprint recognition. The reason is that classification methods reduce the size of the search space to the fingerprints of the same category before matching. First, we suggest a method to extract region of interest (ROI) which have real information about fingerprint from the image. And then we propose a feature extraction method which combines gray level co-occurrence matrix (GLCM) and wavelet features. Finally, we compare the performance of our proposed method with the existing method which use only GLCM as the feature of fingerprint by using the multi-layer perceptron and support vector machine.