• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.307-309
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• 2013

Diligence/Indolence records are the basic data at a human resource management and the essential data for measure salaries of employees. However, since the D/I systems using RFIDs or biometric sensors require hish costs to build and operate the D/I systems, it is difficult for to small business or self-employed business to use them. In this paper, we propose the Diligence/Indolence system to use the location identification of a smart phon. To locate the position of an employee, the WiFi signal of a smart phone and the wireless access point are exploited. Because the pre-owned hardware resources are used and initial building costs are low, it is easy for small business to use the proposed D/I system with low cost.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.325-329
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• 2016

Facial authentication has the limelight because it has less resistance and it is hard to falsify among various biometric identification. The algorithm of facial authentication can bring about huge difference in accuracy and speed by the algorithm construction. Along with face-extracted data by tracing and extracting pupil, the thesis studied algorithm which extracts data to improve error rate and to accurately authenticate face. It detects face by cascade, selects as significant area, divides the facial area into 4 equal parts to save the coordinate of object. Also, to detect pupil from the eye, the binarization is conducted and it detects pupil by Hough conversion. The core coordinate of detected pupil is saved and calculated to conduct facial authentication through data matching. The thesis studied optimized facial authentication algorithm which accurately calculates facial data with pupil trace.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.75-82
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• 2018

As user of mobile device increases, various user authentication methods are actively researched. The user authentication methods includes a method of using a user ID and a password, a method of using user biometric feature, a method of using location based, and a method of authenticating secondary authentication such as OTP(One Time Password) method is used. In this paper, we propose a user system which improves the problem of existing authentication method and encryption can proceed in a way that user desires. The proposed authentication system is composed of an authentication factor collection module that collects authentication factors using a mobile device, a security key generation module that generates a security key by combining the collected authentication factors, and a module that performs authentication using the generated security key module.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2B
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• pp.188-202
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• 2009

In the modern world, where the number of people moving from country to country is sharply increasing, domestic and international driver's licenses are easily fabricated or forged, and distinguishing if a driver's license is legitimate or not is often a difficult task. Furthermore, this would require different countries to mutually share and administer the driving records of individuals, making it a much more complex task (Added to it is the complicated matter of countries having to mutually share and administer the driving records of individuals.) However, the authenticity and security of a driver's license has become the first priority since driver's licenses are also used as identification cards in most countries, thus requiring measures to prevent inappropriate uses arising from theft and embezzlement. In this paper, we propose the mutual authentication mechanism which, can provide enhanced security and efficient operation that is administration of personal information contained within ISO Compliant Driving licence(IDL).

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.159-168
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• 2021

As the information age enters, the use of biometrics using the body is gradually increasing because it is very important to accurately recognize and authenticate each individual's identity for information protection. Among them, finger vein authentication technology is receiving a lot of attention because it is difficult to forge and demodulate, so it has high security, high precision, and easy user acceptance. However, the accuracy may be degraded depending on the algorithm for identification or the surrounding light environment. In this paper, we designed and manufactured a side-type finger vein recognizer that is highly versatile among finger vein measuring devices, and authenticated using the deep learning model of DenseNet-201 for high accuracy and recognition rate. The performance of finger vein authentication technology according to the influence of the infrared light source used and the surrounding visible light was analyzed through simulation. The simulations used data from MMCBNU_6000 of Jeonbuk National University and finger vein images taken directly were used, and the performance were compared and analyzed using the EER.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.23-31
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• 2009

Recently, biometric techniques such as face recognition, finger-print recognition and iris recognition have been widely applied for various applications including door access control, finance security and electric passport. This paper presents the method of using finger-vein pattern for the personal identification. In general, when the finger-vein image is acquired from the camera, various conditions such as the penetrating amount of the infrared light and the camera noise make the segmentation of the vein from the background difficult. This in turn affects the system performance of personal identification. To solve this problem, we propose the novel and fast method for extracting the finger-vein region. The proposed method has two advantages compared to the previous methods. One is that we adopt a locally adaptive thresholding method for the binarization of acquired finger-vein image. Another advantage is that the simple morphological opening and closing are used to remove the segmentation noise to finally obtain the finger-vein region from the skeletonization. Experimental results showed that our proposed method could quickly and exactly extract the finger-vein region without using various kinds of time-consuming filters for preprocessing.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.4
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• pp.63-70
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• 2003

In the modern electronic world, the authentication of a person is an important task in many areas of online-transactions. Using biometrics to authenticate a person's identity has several advantages over the present practices of Personal Identification Numbers(PINs) and passwords. To gain maximum security in the verification system using biometrics, the computation of the verification as well as the store of the biometric pattern has to be taken place in the security token(smart card, USB token). However, there is an open issue of integrating biometrics into the security token because of its limited resources(memory space, processing power). In this paper, we describe our implementation of the USB security token system having 206MHz StrongARM CPU, 16MBytes flash memory, and 1MBytes RAM. Also, we evaluate the performance of a light-weighted In-gerprint verification algorithm that can be executed in the restricted environments. Based on experimental results, we confirmed that the RAM requirement of the proposed algorithm was about 6.8 KBytes and the Equal Error Rate(EER) was 1.7%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.135-141
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• 2011

Recent advances in mobile devices and development of various mobile applications dealing with private information of users made user authentication in mobile devices a very important issue. This paper presents a new user authentication method based on touch screen interfaces. This method uses for authentication the PIN digits as well as the exact locations the user touches to input these digits. Our method is fully compatible with the regular PIN entry method which uses numeric keypads, and it provides better usability than the behavioral biometric schemes because its PIN registration process is much simpler. According to our experiments, our method guarantees EERs of 12.8%, 8.3%, and 9.3% for 4-digit PINs, 6-digit PINs, and 11-digit cell phone numbers, respectively, under the extremely conservative assumption that all users have the same PIN digits and cell phone numbers. Thus we can guarantee much higher performance in identification functionality by applying this result to a more practical situation where every user uses distinct PIN and sell phone number. Finally, our method is far more secure than the regular PIN entry method, which is verified by our experiments where attackers are required to recover a PIN after observing the PIN entry processes of the regular PIN and our method under the same level of security parameters.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.2
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• pp.1-9
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• 2008

As the security requirements of mobile phones have been increasing, there have been extensive researches using one biometric feature (e.g., an iris, a fingerprint, or a face image) for authentication. Due to the limitation of uni-modal biometrics, we propose a method that combines face and iris images in order to improve accuracy in mobile environments. This paper presents four advantages and contributions over previous research. First, in order to capture both face and iris image at fast speed and simultaneously, we use a built-in conventional mega pixel camera in mobile phone, which is revised to capture the NIR (Near-InfraRed) face and iris image. Second, in order to increase the authentication accuracy of face and iris, we propose a score level fusion method based on SVM (Support Vector Machine). Third, to reduce the classification complexities of SVM and intra-variation of face and iris data, we normalize the input face and iris data, respectively. For face, a NIR illuminator and NIR passing filter on camera are used to reduce the illumination variance caused by environmental visible lighting and the consequent saturated region in face by the NIR illuminator is normalized by low processing logarithmic algorithm considering mobile phone. For iris, image transform into polar coordinate and iris code shifting are used for obtaining robust identification accuracy irrespective of image capturing condition. Fourth, to increase the processing speed on mobile phone, we use integer based face and iris authentication algorithms. Experimental results were tested with face and iris images by mega-pixel camera of mobile phone. It showed that the authentication accuracy using SVM was better than those of uni-modal (face or iris), SUM, MAX, NIN and weighted SUM rules.