• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.3
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• pp.49-59
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• 2002

As the electronic commerce increases rapidly in the mobile communication, security issues become more important. A suitable authentication and key agreement for the mobile communication environment is a essential condition. Some protocols based on the public key cryptosystem such as Diffie-Hellman, EIGamal etc. were adapted in the mobile communication. But these protocols that are based on the difficult mathematical problem in the algebra, are so slow and have long key-length. Therefore, these have many limitation to apply to the mobile communication. In this paper, we propose an authentication and key agreement protocol based on NTRU to overcome the restriction of the mobile communication environment such as limited sources. low computational fewer, and narrow bandwidth. The proposed protocol is faster than other protocols based on ECC, because of addition and shift operation with small numbers in the truncated polynomial ring. And it is as secure as other existent mathematical problem because it is based on finding the Shortest or Closest Vector Problem(SVP/CVP).

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.757-758
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• 2006

In this paper, the palmprint classification and recognition method based on PCA (Principal Components Analysis) using the dimension reduction of singular vector is proposed. And the 135dpi palmprint image which is obtained by the palmprint acquisition device is used for the effectual palmprint recognition system. The proposed system consists of the palmprint acquisition device, DB generation algorithm and the palmprint recognition algorithm. The palmprint recognition step is limited 2 times. As a results, GAR and FAR are 98.5% and 0.036%.

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.9-16
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• 2005

This paper focuses on the study of the face authentication system and the robustness of fact authentication methods under illumination changes. Four different face authentication methods are tried. These methods are as fellows; PCA(Principal Component Analysis), GMM(Gaussian Mixture Modeis), 1D HMM(1 Dimensional Hidden Markov Models), Pseudo 2D HMM(Pseudo 2 Dimensional Hidden Markov Models). Experiment results involving an artificial illumination change to fate images are compared with each other. Face feature vector extraction based on the 2D DCT(2 Dimensional Discrete Cosine Transform) if used. Experiments to evaluate the above four different fate authentication methods are carried out on the ORL(Olivetti Research Laboratory) face database. Experiment results show the EER(Equal Error Rate) performance degrade in ail occasions for the varying ${\delta}$. For the non illumination changes, Pseudo 2D HMM is $2.54{\%}$,1D HMM is $3.18{\%}$, PCA is $11.7{\%}$, GMM is $13.38{\%}$. The 1D HMM have the bettor performance than PCA where there is no illumination changes. But the 1D HMM have worse performance than PCA where there is large illumination changes(${\delta}{\geq}40$). For the Pseudo 2D HMM, The best EER performance is observed regardless of the illumination changes.

• 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 the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1427-1437
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• 2018

While biometric authentication system has been in general use, research is ongoing to apply biometric data to public key infrastructure. It is a significant task to generate a cryptographic key from biometrics in setting up a public key of Bio-PKI. Methods for generating the key by quantization of feature vector can cause data loss and degrade the performance of key extraction. In this paper, we suggest a new method for generating a cryptographic key from classification results of biometric data using multiple classifying models. Our proposal does not cause data loss of feature vector so it showed better performance in key extraction. Also, it uses the multiple models to generate key blocks which produce sufficient length of the key.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2658-2681
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• 2023

In edge computing scenarios, IoT end devices play a crucial role in relaying and forwarding data to significantly improve IoT network performance. However, traditional routing mechanisms are not applicable to this scenario due to differences in network size and environment. Therefore, it becomes crucial to establish an effective and reliable data transmission path to ensure secure communication between devices. In this paper, we propose a trusted path selection strategy that comprehensively considers multiple attributes, such as link stability and edge cooperation, and selects a stable and secure data transmission path based on the link life cycle, energy level, trust level, and authentication status. In addition, we propose the Stability-based On-demand Multipath Distance Vector (STAOMDV) protocol based on the Ad hoc AOMDV protocol. The STAOMDV protocol implements the collection and updating of link stability attributes during the route discovery and maintenance process. By integrating the STAOMDV protocol with the proposed path selection strategy, a dependable and efficient routing mechanism is established for IoT networks in edge computing scenarios. Simulation results validate that the proposed STAOMDV model achieves a balance in network energy consumption and extends the overall network lifespan.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.75-85
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• 2010

This paper presents a robust 3D model hashing based on object feature vector for 3D content authentication. The proposed 3D model hashing selects the feature objects with highest area in a 3D model with various objects and groups the distances of the normalized vertices in the feature objects. Then we permute groups in each objects by using a permutation key and generate the final binary hash through the binary process with the group coefficients and a random key. Therefore, the hash robustness can be improved by the group coefficient from the distance distribution of vertices in each object group and th hash uniqueness can be improved by the binary process with a permutation key and a random key. From experimental results, we verified that the proposed hashing has both the robustness against various mesh and geometric editing and the uniqueness.

• Journal of the Korea Convergence Society
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• v.11 no.2
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• pp.17-22
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• 2020

In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.157-163
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• 2005

In this paper, we present the multimodal system based on the fusion of two user-friendly biometric modalities: Iris and Face. In order to reach robust identification and verification we are going to combine two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. In addition, the Synergetic Neural Network(SNN) is used to obtain matching score of the preprocessed data. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6063-6079
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• 2019

When the length of sampling data sequence is too large, the method of gesture recognition based on traditional Dynamic Time Warping (DTW) algorithm will lead to too long calculation time, and the accuracy of recognition result is not high.Support vector machine (SVM) has some shortcomings in precision, Edit Distance on Real Sequences(EDR) algorithm does not guarantee that noise suppression will not suppress effective data.A new method based on Improved Interpolation Dynamic Time Warping (IIDTW)algorithm is proposed to improve the efficiency of gesture recognition and the accuracy of gesture recognition. The results show that the computational efficiency of IIDTW algorithm is more than twice that of SVM-DTW algorithm, the error acceptance rate is FAR reduced by 0.01%, and the error rejection rate FRR is reduced by 0.5%.Gesture recognition based on IIDTW algorithm can achieve better recognition status. If it is applied to unlock mobile phone, it is expected to become a new generation of unlock mode.