• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1007-1017
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• 2019

Currently, most biometrics system authenticates users by using single biometric information. This method has many problems such as noise problem, sensitivity to data, spoofing, a limitation of recognition rate. One method to solve this problems is to use multi biometric information. The multi biometric authentication system performs information fusion for each biometric information to generate new information, and then uses the new information to authenticate the user. Among information fusion methods, a score-level fusion method is widely used. However, there is a problem that a normalization operation is required, and even if data is same, the recognition rate varies depending on the normalization method. A rank-level fusion method that does not require normalization is proposed. However, a existing rank-level fusion methods have lower recognition rate than score-level fusion methods. To solve this problem, we propose a rank-level fusion method with higher recognition rate than a score-level fusion method using correlation coefficient. The experiment compares recognition rate of a existing rank-level fusion methods with the recognition rate of proposed method using iris information(CASIA V3) and face information(FERET V1). We also compare with score-level fusion methods. As a result, the recognition rate improve from about 0.3% to 3.3%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.1
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• pp.69-79
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• 2016

Biometric authentication is considered as being an efficient authentication method, since a user is not required to possess or memorize any other information other than biometrics. However, since biometric information is sensitive and could be permanently unavailable in case of revealing that information just once, it is essential to preserve privacy of biometrics. In addition, since noise is inherent in the user of biometric recognition technologies, the biometric authentication needs to handle the noise. Recently, biometric authentication protocols using fuzzy extractor have been actively researched, but the fuzzy extractor-based authentication has a problem that a user should memorize an additional information, called helper data, to deal with their noisy biometric information. In this paper, we propose a novel biometric authentication protocol using Hidden Vector Key Encapsulation Mechanism(HV-KEM) which is one of functional encryption schemes. A primary advantage of our protocol is that a user does not need to possess or memorize any additional information. We propose security requirements of HV-KEM necessary for constructing biometric authentication protocols, and analyze our proposed protocol in terms of correctness, security, and efficiency.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.173-179
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• 2013

In a biometric authentication scheme, a user's biometric data that is unique to the user is used to prove the user's identity to the third party. Since the user should have to participate in every authentication sessions, it's not possible to delegate other users to authenticate instead of himself/herself. In a biometric signature scheme, contrary to authentication scheme, a user's biometric data is used to prove that "this message is signed by the signer who claims to be" to the third party. However, once the biometric key is created, it can be accessed by the signer. Thus, it's possible to lend the biometric key to other users. In this study, the server based biometric realtime signature scheme is proposed. The proposed scheme can be applied to sign the vote in electronic voting or to authenticate the copyright owner in DRM enabled mobile commerce where the proxy signatures are not allowed.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.671-676
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• 2016

Recently, according to the limelight of Fintech, simple payment using biometric at smartphone is widely used. In this paper, we propose a new contactless biometric method using thumb image without additional sensors unlike previous biometrics such as fingerprint, iris, and vein recognition. In our method, length, width, and skin texture information are used as features. For that, illumination normalization, skin region segmentation, size normalization and alignment procedures are sequentially performed from the captured thumb image. Then, correlation coefficient is calculated for similarity measurement. To analyze recognition accuracy, genuine and imposter matchings are performed. At result, we confirmed the FAR of 1.68% at the FRR of 1.55%. In here, because the distribution of imposter matching is almost normal distribution, our method has the advantage of low FAR. That is, because 0% FAR can be achieved at the FRR of 15%, the proposed method is enough to 1:1 matching for payment verification.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.65-72
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• 2020

In this paper, a USB module with fingerprint recognition was designed as a distributed node of blockchain on distributed ID (DID, distributed ID) for user identification. This biometric-linked fingerprint recognition device was verified for the real-time authentication process of authentication transaction with FIDO(Fast IDentity Online) server. Blockchain DID-based services were proposed like as a method of individual TV rating survey, and recommending service for customized shopping channels, and crypto-currency, too. This DID based remote service can be improved by recognizing of channel-changing information through personal identification. The proposed information of production purchase can be shared by blockchain. And customized service can be provided for the utilization of purchase history in shoppingmall using distributed ID. As a result, this blockchain node-device and Samsung S10 Key-srore with FIDO service can be certified for additional transactions through various biometric authentication like fingerprint, and face recognition.

• Journal of the Microelectronics and Packaging Society
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• v.23 no.2
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• pp.29-35
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• 2016

With the pace of rapid innovation in technology of IoT (Internet of Things) and smart devices, biometric technology becomes one of the most progressive industries. Recent trends in biometrics show most are focused on embedding biometric sensors in mobile devices for user authentication. Multifactor biometrics such as fingerprint, retina, voice, etc. are considering as identification system to provide users with services more secured and convenient. Here we, therefore, demonstrate some major technologies and market trends of mobile biometric technology with its concerns and issues.

• Journal of the Korea Convergence Society
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• v.8 no.2
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• pp.9-14
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• 2017

A fingerprint is unique to each person and can be represented as a digital form. As the fingerprint is the part of human body, fingerprint recognition is much more easy to use and secure rather than using password or resident card for user authentication. In addition, as the newly released smart phones have built-in camera and fingerprint sensors, the demand for biometric authentication is increasing rapidly. But, the drawback is that the fingerprint can be counterfeited easily and if it's exposed to the hacker, it cannot be reused. Thus, the original fingerprint template should be transformed for registration and authentication purposes. Existing transformation functions use passcode to transform the original template to the cancelable form. Additional module is needed to input the passcode, so it requires more cost and lowers the usability. In this paper, we propose biometric authentication scheme that is economic and easy to use. The proposed scheme is consisted of cancelable biometric template creation, registration and user authentication protocols, and can control several security levels by configuring the number of fingerprints and scan times. We also analyzed that our scheme is secure against the brute-force attack and the active attacks.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.100-105
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• 2017

This work presents an ECG biometric recognition system for the purpose of biometric authentication. ECG biometric approaches are divided into two major categories, fiducial-based and non-fiducial-based methods. This paper proposes a new non-fiducial framework using discrete cosine transform and a Random Forest classifier. When using DCT, most of the signal information tends to be concentrated in a few low-frequency components. In order to apply feature vector of Random Forest, DCT feature vectors of ECG heartbeats are constructed by using the first 40 DCT coefficients. RF is based on the computation of a large number of decision trees. It is relatively fast, robust and inherently suitable for multi-class problems. Furthermore, it trade-off threshold between admission and rejection of ID inside RF classifier. As a result, proposed method offers 99.9% recognition rates when tested on MIT-BIH NSRDB.

• Journal of Biomedical Engineering Research
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• v.31 no.5
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• pp.365-374
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• 2010

This paper is about the personal identification algorithm using an ECG that has been studied by a few researchers recently. Previously published algorithm can be classified as two methods. One is the method that analyzes ECG features and the other is the morphological analysis of ECG. The main characteristic of proposed algorithm uses together two methods. The algorithm consists of training and testing procedures. In training procedure, the features of all recognition objects' ECG were extracted and the PCA was performed for morphological analysis of ECG. In testing procedure, 6 candidate ECG's were chosen by morphological analysis and then the analysis of features among candidate ECG's was performed for final recognition. We choose 18 ECG files from MIT-BIH Normal Sinus Rhythm Database for estimating algorithm performance. The algorithm extracts 100 heartbeats from each ECG file, and use 40 heartbeats for training and 60 heartbeats for testing. The proposed algorithm shows clearly superior performance in all ECG data, amounting to 90.96% heartbeat recognition rate and 100% ECG recognition rate.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.499-507
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• 2006

Recently human gait has been considered as a useful biometric supporting high performance human identification systems. This paper proposes a view-based pedestrian identification method using the dynamic silhouettes of a human body modeled with the Hidden Markov Model(HMM). Two types of gait models have been developed both with an endless cycle architecture: one is a discrete HMM method using a self-organizing map-based VQ codebook and the other is a continuous HMM method using feature vectors transformed into a PCA space. Experimental results showed a consistent performance trend over a range of model parameters and the recognition rate up to 88.1%. Compared with other methods, the proposed models and techniques are believed to have a sufficient potential for a successful application to gait recognition.