• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• Mass Spectrometry Letters
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• v.13 no.4
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• pp.106-114
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• 2022

A rapid and reliable approach to the identification of microorganisms is a critical requirement for large-scale culturomics analysis. MALDI-TOF MS is a suitable technique that can be a better alternative to conventional biochemical and gene sequencing methods as it is economical both in terms of cost and labor. In this review, the applications of MALDI-TOF MS for the comprehensive identification of microorganisms and bacterial strain typing for culturomics-based approaches for various environmental studies including bioremediation, plant sciences, agriculture and food microbiology have been widely explored. However, the restriction of this technique is attributed to insufficient coverage of the mass spectral database. To improve the applications of this technique for the identification of novel isolates, the spectral database should be updated with the peptide mass fingerprint (PMF) of type strains with not only microbes with clinical relevance but also from various environmental sources. Further, the development of enhanced sample processing methods and new algorithms for automation and de-replication of isolates will increase its application in microbial ecology studies.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.751-753
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• 2005

지문을 5가지 클래스로 나누는 헨리시스템을 기반으로 신경망이나 SVM(Support Vector Machines) 등과 같은 다양한 패턴분류 기법들이 지문분류에 많이 사용되고 있다. 특히 최근에는 높은 분류 성능을 보이는 SVM 분류기의 결합을 이용한 연구가 활발히 진행되고 있다. 지문은 클래스 구분이 모호한 영상이 많아서 단일결합모델로는 분류에 한계가 있다. 이를 위해 본 논문에서는 새로운 분류기 결합모델인 다중결정템플릿(Multiple Decision Templates, MuDTs)을 제안한다. 이 방법은 하나의 지문클래스로부터 서로 다른 특성을 갖는 클러스터들을 추출하여 각 클러스터에 적합한 결합모델을 생성한다. NIST-database4 데이터로부터 추출한 핑거코드에 대해 실험한 결과. 5클래스와 4클래스 분류문제에 대하여 각각 $90.4\%$와 $94.9\%$의 분류성능(거부율 $1.8\%$)을 획득하였다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.87-101
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• 2018

Minors' law evasion is getting serious. Some of them make a fake ID card or use a stolen ID card illegally. They are illegally used for buying alcohol, cigarette, bond or going to club, pub, motel and so on. On the latest News, there was a case that teens rented a car with a driver licence without checking and had severe car accident. Like this case, car rental service was fined and suspended from business because they did not figure out ID card's counterfeit or modification. Thus, this study investigates the detecting method about the ID card(including a driver license) counterfeit, modification or illegal use. The server has a database of 17~19's minors one fingerprint and services whether the visitor is adult true or not. The method will be usable and economical for business. Then, it was compared and analyzed with methods already in use in business.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.1-8
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• 2015

In recent years, ear has emerged as a new biometric trait, because it has advantage of higher user acceptance than fingerprint and can be captured at remote distance in an indoor or outdoor environment. This paper proposes an individual identification method using ear region based on SIFT(shift invariant feature transform). Unlike most of the previous studies using rectangle shape for extracting a region of interest(ROI), this study sets an ROI as a flexible expanded region including ear. It also presents an effective extraction and matching method for SIFT keypoints. Experiments for evaluating the performance of the proposed method were performed on IITD public database. It showed correct identification rate of 98.89%, and it showed 98.44% with a deformed dataset of 20% occlusion. These results show that the proposed method is effective in ear recognition and robust to occlusion.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• Journal of Internet of Things and Convergence
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• v.2 no.4
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• pp.35-39
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• 2016

This paper is about IoT(Internet of Things)-based indoor localization scheme. GPS and WiFi are widely used to estimate the location of things. However, GPS has drawback of poor reception and radio disturbance in doors. To estimate the location in WiFi-based method, the user collects the information by scanning nearby WiFi(s) and transferring the information to WiFi database server. This is a fingerprint method with disadvantage of having an additional DB server. IoT is the internetworking of things, and this is on rapid rise. I propose the IoT-based indoor localization scheme. Under the proposed method, a device internetworking with another device with its own location information like GPS coordinate can estimate its own location through RSSI. With more devices localizing its own, the localization accuracy goes high. The proposed method allows the user to estimate the location without GPS and WiFi DB server.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.898-903
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• 2020

In this paper, we propose an indoor logistic tracking system that identifies the location and inventory of the logistics in the room based on fingerprints. Through this, we constructed the actual infrastructure of the logistics center and designed and implemented the logistics management system. The proposed system collects the signal strength through the location terminal and generates the signal map to locate the goods. The location terminal is composed of a UHF RFID reader and a wireless LAN card, reads the peripheral RFID signal and the signal of the wireless AP, and transmits it to the web server. The web server processes the signal received from the location terminal and stores it in the database, and the user uses the data to produce the signal map. The proposed system combines UHF RFID with existing fingerprinting method to improve performance in the environment of querying multiple objects.

• Journal of Korea Multimedia Society
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• v.14 no.3
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• pp.433-439
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• 2011

The positioning system based on wireless AP collects AP information distributed in the real world, stores it into database, and measures the position objects by comparing with searched AP information. The existing fingerprinting method is a probabilistic modeling method that acquires much of the data collected from one location upon database composition, and stores the average of the data for the sake of use it in positioning objects. Using the average value, however, may cause the probability of errors Such errors are fatal weaknesses for services based on the accurate position. This paper described the characteristics and problems of the previously used wireless AP positioning system, and proposed a method of using the AP DB and an MSS mechanism for outdoor positioning in order to solve the aforementioned problems. And the results obtained from experimental tests showed that the proposed method achieved very low error rate(27%) compared with the existing method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.553-559
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• 2004

In todays security industry, personal identification is also based on biometric. Biometric identification is performed basing on the measurement and comparison of physiological and behavioral characteristics, Biometric for recognition includes voice dynamics, signature dynamics, hand geometry, fingerprint, iris, etc. Iris can serve as a kind of living passport or living password. Iris recognition system is the one of the most reliable biometrics recognition system. This is applied to client/server system such as the electronic commerce and electronic banking from stand-alone system or networks, ATMs, etc. A new algorithm using nonlinear function in recognition process is proposed in this paper. An algorithm is proposed to determine the localized iris from the iris image received from iris input camera in client. For the first step, the algorithm determines the center of pupil. For the second step, the algorithm determines the outer boundary of the iris and the pupillary boundary. The localized iris area is transform into polar coordinates. After performing three times Wavelet transformation, normalization was done using sigmoid function. The converting binary process performs normalized value of pixel from 0 to 255 to be binary value, and then the converting binary process is compare pairs of two adjacent pixels. The binary code of the iris is transmitted to the by server. the network. In the server, the comparing process compares the binary value of presented iris to the reference value in the University database. Process of recognition or rejection is dependent on the value of Hamming Distance. After matching the binary value of presented iris with the database stored in the server, the result is transmitted to the client.