• Korean Journal of Optics and Photonics
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• v.9 no.6
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• pp.385-389
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• 1998

In this paper, we propose an optical correlator system using volume holograms for database of matched filters. Optical correlator has high speed and parallel processing characteristics of optics. Matched filters are recorded into a volume hologram that can store data with high density, transfer them with high speed, and select a randomly chosen data element. The multiple reference images of database are prerecorded in a photorefractive crystal in the form of Fourier transform images, simply by passing the image displayed in a spatial light modulator through a Fourier transform lens. The angular multiplexing method for multiple holograms of database can be achieved by rotating the crystal by use of a step motor. Experimental results show that the proposed system can be used for the fingerprint recognition.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.55-66
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• 2021

In this paper, we propose a signal propagation modeling technique for generating a positioning fingerprint DB based on Long Term Evolution (LTE) signals. When a DB is created based on the location-based signal information collected in an urban area, gaps in the DB due to uncollected areas occur. The spatial interpolation method for filling the gaps has limitations. In addition, the existing gap filling technique through signal propagation modeling does not reflect the signal attenuation characteristics according to directions occurring in urban areas by considering only the signal attenuation characteristics according to distance. To solve this problem, this paper proposes a Deep Neural Network (DNN)-based signal propagation functionalization technique that considers distance and direction together. To verify the performance of this technique, an experiment was conducted in Seocho-gu, Seoul. Based on the acquired signals, signal propagation characteristics were modeled for each method, and Root Mean Squared Errors (RMSE) was calculated using the verification data to perform comparative analysis. As a result, it was shown that the proposed technique is improved by about 4.284 dBm compared to the existing signal propagation model. Through this, it can be confirmed that the DNN-based signal propagation model proposed in this paper is excellent in performance, and it is expected that the positioning performance will be improved based on the fingerprint DB generated through it.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.203-211
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• 2005

In this paper, we propose a new automatic fingerprint identification system that identifies individuals in large databases. The algorithm consists of three steps; preprocessing, classification, and matching, in the classification. we present a new classification technique based on the statistical approach for directional image distribution. In matching, we also describe improved minutiae candidate pair extraction algorithm that is faster and more accurate than existing algorithm. In matching stage, we extract fingerprint minutiaes from its thinned image for accuracy, and introduce matching process using minutiae linking information. Introduction of linking information into the minutiae matching process is a simple but accurate way, which solves the problem of reference minutiae pair selection in comparison stage of two fingerprints quickly. This algorithm is invariant to translation and rotation of fingerprint. The proposed system was tested on 1000 fingerprint images from the semiconductor chip style scanner. Experimental results reveal false acceptance rate is decreased and genuine acceptance rate is increased than existing method.

• 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 KIISE:Software and Applications
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• v.31 no.7
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• pp.959-967
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• 2004

Jain et al. proposed the hybrid matching method which was combined the minutia-based matching method and the filter-bank based matching method. And, their experimental results proved the hybrid matching method was more effective than each of them. However, this hybrid method cannot utilize each peculiar advantage of two methods. The reason is that it gets the matching score by simply summing up each weighted matching score after executing two methods individually. In this paper, we propose new hybrid matching method. It mixes two matching methods during the feature extraction process. This new hybrid method has lower ERR than the filter-bank based method and higher ERR than the minutia-based method. So, we propose the adaptive hybrid scoring method, which selects the matching score in order to preserve the characteristics of two matching methods. Using this method, we can get lower ERR than the hybrid matcher by Jain et al. Experimental results indicate that the proposed methods can improve the matching performance up to about 1% in ERR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.1102-1107
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• 2004

Fingerprint Recognition System is made up of Off-line treatment and On-line treatment; the one is registering all the information of there trieving features which are retrieved in the digitalized fingerprint getting out of the analog fingerprint through the fingerprint acquisition device and the other is the treatment making the decision whether the users are approved to be accessed to the system or not with matching them with the fingerprint features which are retrieved and database from the input fingerprint when the users are approaching the system to use. In matching between On-line and Off-line treatment, the most important thing is which features we are going to use as the standard. Therefore, we have been using “Delta” and “Core” as this standard until now, but there might have been some deficits not to exist in every person when we set them up as the standards. In order to handle the users who do not have those features, we are still using the matching method which enables us to make up of the spanning tree or the triangulation with the relations of the spanned feature. However, there are some overheads of the time on these methods and it is not sure whether they make the correct matching or not. Therefore, I would like to represent the more correct matching algorism in this paper which has not only better matching rate but also lower mismatching rate compared to the present matching algorism by selecting the line segment connecting two minutiae on the same ridge and furrow structures as the reference point.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.69-80
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• 1999

Recently, social requirements of personal identification techniques are rapidly expanding in a number of new application ares. Especially fingerprint recognition is the most important technology. Fingerprint recognition technologies are well established, proven, cost and legally accepted. Therefore, it has more spot lighted among the any other biometrics technologies. In this paper we propose a new on-line fingerprint recognition algorithm for non-inked type live scanner to fit their increasing of security level under the computing environment. Fingerprint recognition system consists of two distinct structural blocks: feature extraction and feature matching. The main topic in this paper focuses on the feature matching using the fingerprint minutiae (ridge ending and bifurcation). Minutiae matching is composed in the alignment stage and matching stage. Success of optimizing the alignment stage is the key of real-time (on-line) fingerprint recognition. Proposed alignment algorithm using clique shows the strength in the search space optimization and partially incomplete image. We make our own database to get the generality. Using the traditional statistical discriminant analysis, 0.05% false acceptance rate (FAR) at 8.83% false rejection rate (FRR) in 1.55 second average matching speed on a Pentium system have been achieved. This makes it possible to construct high performance fingerprint recognition system.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1929-1932
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• 2003

자동 지문 인식시스템은 대부분의 사용자 인증 과정을 처리하기 위해 사용하는 아이디/비밀번호 방법의 보안상 결점인 비밀번호 유출로 인한 보안 위협을 근본적으로 제거할 수 있다. 그러나 아직까지 수십만에서 수천만 건에 이른 대용량 지문 검색, 서버에 대한 성능이 입증된 것이 없으며 본 연구에서는 지문분류, 대용량 지문 검색, 다중서버, 다중쓰레드. 인증서버 기술을 갖는 대용량 자동 지문 인식 시스템을 연구하였다. 그리고 10만개의 지문 데이터를 기준으로 지문 검색에 대한 성능을 실험하였다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.