• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1002-1003
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• 2019

Face recognition is an extensively researched area of computer vision. Visible, infrared, thermal, and 3D modalities have been used against various challenges of face recognition such as illumination, pose, expression, partial information, and disguise. In this paper we present a multi-modal approach to face recognition using convolutional neural networks. We use visible and thermal face images as two separate inputs to a multi-input deep learning network for face recognition. The experiments are performed on IRIS visible and thermal face database and high face verification rates are achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.591-608
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• 2023

With the emergence of advanced encryption technologies such as Quantum Cryptography and Full Disk Encryption, an era of strengthening information security has begun. Users respond positively to the advancement of privacy-enhancing technology, on the other hand, investigative agencies have difficulty unveiling the actual truth as they fail to decrypt devices. In particular, unlike past ciphers, encryption methods using biometric information such as fingerprints, iris, and faces have become common and have faced technical limitations in collecting digital evidence. Accordingly, normative solutions have emerged as a major issue. The United States enacted the CLOUD Act with the legal mechanism of 'Contempt of court' and in 2016, the United Kingdom substantiated the Compelled Decryption through the Investigatory Powers Act (IPA). However, it is difficult to enforce Compelled Decryption on individuals in Korea because Korean is highly sensitive to personal information. Therefore, in this paper, we sought a method of introducing a Compelled Decryption that does not contradict the people's legal sentiment through a perception survey of 95 people on the Compelled Decryption. We tried to compare and review the Budapest Convention with major overseas laws such as the United States and the United Kingdom, and to suggest a direction of legislation acceptable to the people in ways to minimize infringement of privacy. We hope that this study will be an effective legal response plan for law enforcement agencies that can normatively overcome the technical limitations of decoding.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.59-67
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• 2015

A novel authentication mechanism is biometric authentication where users are identified by their measurable human characteristics, such as fingerprint, voiceprint, and iris scan. The technology of biometrics is becoming a popular method for engineers to design a more secure user authentication scheme. In terms of physiological and behavioral human characteristics, biometrics is used as a form of identity access management and access control, and it services to identity individuals in groups that are under surveillance. In this article, we review the biometric-based authentication protocol by Althobati et al. and provide a security analysis on the scheme. Our analysis shows that Althobati et al.'s scheme does not guarantee server-to-user authentication. The contribution of the current work is to demonstrate this by mounting threat of data integrity and bypassing the gateway node on Althobati et al.'s scheme. In addition, we analysis the security vulnerabilities of Althobati et al.'s protocol.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.685-691
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• 2008

The selection of classification boundaries in fuzzy rule- based classification systems is an important and difficult problem. So various methods based on learning processes such as neural network, genetic algorithm, and so on have been proposed for it. In a previous study, we pointed out the limitation of the methods and discussed a method for fuzzy partitioning in the overlapped region on feature space in order to overcome the time-consuming when the additional parameters for tuning fuzzy membership functions are necessary. In this paper, we propose a method to determine three types of classification boundaries(i.e., non-overlapping, overlapping, and a boundary point) on the basis of statistical information of the given dataset without learning by extending the method described in the study. Finally, we show the effectiveness of the proposed method through experimental results applied to pattern classification problems using the modified IRIS and standard IRIS datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6152-6168
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• 2017

Recently, the biometric-based authentication systems such as FIDO (Fast Identity Online) are increased in mobile computing environments. The biometric-based authentication systems are performed on the mobile devices with the battery, the improving energy efficiency is important issue. In the case, the size of images (i.e., face, fingerprint, iris, and etc.) affects both recognition accuracy and energy consumption, and hence the tradeoff analysis between the both recognition accuracy and energy consumption is necessary. In this paper, we propose an energy-efficient way to authenticate based on biometric information with tradeoff analysis between the both recognition accuracy and energy consumption in multimedia IoT (Internet of Things) transmission environments. We select the facial information among biometric information, and especially consider the multicore-based mobile devices. Based on our experimental results, we prove that the proposed approach can enhance the energy efficiency of GABOR+LBP+GRAY VALUE, GABOR+LBP, GABOR, and LBP by factors of 6.8, 3.6, 3.6, and 2.4 over the baseline, respectively, while satisfying user's face recognition accuracy.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• International conference on construction engineering and project management
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• 2011.02a
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• pp.311-317
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• 2011

There is a growing concern in reducing greenhouse gas emissions all over the world. The U.K. has set 34% target reduction of emission before 2020 and 80% before 2050 compared to 1990 recently in Post Copenhagen Report on Climate Change. In practise, Life Cycle Cost (LCC) and Life Cycle Assessment (LCA) tools have been introduced to construction industry in order to achieve this such as. However, there is clear a disconnection between costs and environmental impacts over the life cycle of a built asset when using these two tools. Besides, the changes in Information and Communication Technologies (ICTs) lead to a change in the way information is represented, in particular, information is being fed more easily and distributed more quickly to different stakeholders by the use of tool such as the Building Information Modelling (BIM), with little consideration on incorporating LCC and LCA and their maximised usage within the BIM environment. The aim of this paper is to propose the development of a model-based LCC and LCA tool in order to provide sustainable building design decisions for clients, architects and quantity surveyors, by then an optimal investment decision can be made by studying the trade-off between costs and environmental impacts. An application framework is also proposed finally as the future work that shows how the proposed model can be incorporated into the BIM environment in practise.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.131-134
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• 2002

As the increased use of computer, wired/wireless/mobile Internet, security in using Internet becomes a more important problem. Thus, biometric technology using physical and behavior characteristics of a person is hot issue. Many different types of biometric technologies of a person such as fingerprint, face, iris, vein, DNA, brain wave, palm, voice, dynamic signature, etc. had already been studied but remained unsuccessful because they do not meet social demands. However, recently many of these technologies have been actively revived and researchers have developed new products on various commercial fields. Dynamic signature verification technology is to verify the signer by calculating his writing manner, speed, angle, and the number of strokes, order, the down/up/movement of pen when the signer input his signature with an electronic pen for his authentication. Then signature verification system collects mentioned above various feature information and compares it with the original one and simultaneously analyzes to decide whether signature is forgery or true. The prospect of signature verification technology is very promising and its use will be wide spread in terms of economy, security, practicality, stability and convenience.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.373-376
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• 2013

Nowadays, eyes detection is required and considered as the most important step in several applications, such as eye tracking, face identification and recognition, facial expression analysis and iris detection. This paper presents the eyes detection in facial images using Harris corner detection. Firstly, Haar-like features for face detection is used to detect a face region in an image. To separate the region of the eyes from a whole face region, the projection function is applied in this paper. At the last step, Harris corner detection is used to detect the eyes location. In experimental results, the eyes location on both grayscale and color facial images were detected accurately and effectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.753-754
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• 2016

스트레스는 매일 살아가면서 사소한 일부터 큰 충격을 받는 삶의 주요한 사건까지 다양한 요인으로 부터 온다. 이는 어느 한 시기에만 나타나는 것이 아니라 인간의 전 생애에 거쳐 나타나므로 지속적으로 스트레스를 관리할 수 있는 연구가 필요하다. 이에 본 논문은 홍채의 형태, 균열, 색깔, 고리, 주름 등의 특징들을 분석하여 환자의 병을 진단하는 홍채진단을 이용하여 스트레스 지수를 측정하는 시스템을 개발하였다. 개발한 시스템은 홍채의 특징인 스트레스 고리를 검출하고, 스트레스 설문지의 결과와 함께 분석하여 안드로이드 어플리케이션을 통해 사용자에게 스트레스 정도를 제공한다. 제안한 시스템은 사용자가 언제 어디서나 쉽게 현재 자신이 느끼고 있는 스트레스 정도를 알 수 있고 이에 상응하는 예측되는 질병에 대해 예방하여 조기 진단 및 질병 예방의 하나의 방법으로 사용할 수 있다.