• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• East Asian mathematical journal
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• v.36 no.5
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• pp.593-604
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• 2020

Let the circle act on a compact oriented manifold with a discrete fixed point set. At each fixed point, there are positive integers called weights, which describe the local action of S¹ near the fixed point. In this paper, we provide the author's original proof that only uses the Atiyah-Singer index formula for the classification of the weights at the fixed points if the dimension of the manifold is 4 and there are at most 4 fixed points, which made the author possible to give a classification for any finite number of fixed points.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.892-905
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• 2018

In smart home environment, certificate based signature technology is being studied by communication with Internet of Things(IoT) device. However, block - chain technology has attracted much attention because of the problems such as single - point error and management overhead of the trust server. Among them, Keyless Signature Infrastructure(KSI) provides integrity by configuring user authentication and global timestamp of distributed server into block chain by using hash-based one-time key. In this paper, we provide confidentiality by applying group key and key management based on multi - solution chain. In addition, we propose a smart home environment that can reduce the storage space by using Extended Merkle Tree and secure and efficient KSI-based authentication and communication with enhanced security strength.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.1C
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• pp.93-101
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• 2006

Proxy signature schemes based on delegation of warrant are frequently studied in these days. Proxy signature schemes that used in these days have some problems about the security. Especially, it is difficult to prevent misuse of certification and private key. In this thesis, we propose the more stronger security structure by turning the point from original signer with proxy signer to original signer with verifier, and the proposed protocol is more stronger than previous scheme and can be applied to various PKI based application.

• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.555-562
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• 2009

In malware accident investigation, the most important thing is detection of malicious code. Signature based anti-virus softwares have been used in most of the accident. Malware can easily avoid signature based detection by using packing or encryption method. Because of this, packed file detection is also important. Detection methods can be divided into signature based detection and entropy based detection. Signature based detection can not detect new packing. And entropy based detection has a problem with false positive. We provides detection method using entropy statistics of entry point section and 'write' properties of essential characteristic of packed file. And then, we show packing detection tool and evaluate its performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.539-544
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• 2013

In this paper, we was designed three-dimensional face recognition algorithm using polynomial based RBFNNs and proposed method to calculate the recognition performance. In case of two-dimensional face recognition, the recognition performance is reduced by the external environment like facial pose and lighting. In order to compensate for these shortcomings, we perform face recognition by obtaining three-dimensional images. obtain face image using three-dimension scanner before the face recognition and obtain the front facial form using pose-compensation. And the depth value of the face is extracting using Point Signature method. The extracted data as high-dimensional data may cause problems in accompany the training and recognition. so use dimension reduction data using PCA algorithm. accompany parameter optimization using optimization algorithm for effective training. Each recognition performance confirm using PSO, DE, GA algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.748-753
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• 2012

In this paper, Face recognition algorithm is designed based on optimized pRBFNNs pattern classifier using three-dimensional scanner. Generally two-dimensional image-based face recognition system enables us to extract the facial features using gray-level of images. The environmental variation parameters such as natural sunlight, artificial light and face pose lead to the deterioration of the performance of the system. In this paper, the proposed face recognition algorithm is designed by using three-dimensional scanner to overcome the drawback of two-dimensional face recognition system. First face shape is scanned using three-dimensional scanner and then the pose of scanned face is converted to front image through pose compensation process. Secondly, data with face depth is extracted using point signature method. Finally, the recognition performance is confirmed by using the optimized pRBFNNs for solving high-dimensional pattern recognition problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.106-112
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• 2015

In this paper, we propose an on-line signature recognition algorithm using fingertip point in the air from the depth image acquired by Kinect. We use ten statistical features for each X, Y, Z axis to react to changes in Shifting and Scaling of the signature trajectories in three-dimensional space. Artificial Neural Network is a machine learning algorithm used as a tool to solve the complex classification problem in pattern recognition. We implement the proposed algorithm to actual on-line signature recognition system. In experiment, we verify the proposed method is successful to classify 4 different on-line signatures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1122-1127
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• 2014

WLAN is affordability, flexibility, and ease of installation, use the smart device due to the dissemination and the AP (Access Point) to the simplification of the Office building, store, at school. Wi-Fi radio waves because it uses the medium of air transport to reach areas where security threats are always exposed to illegal AP installation, policy violations AP, packet monitoring, AP illegal access, external and service access, wireless network sharing, MAC address, such as a new security threat to steal. In this paper, signature-based of wireless intrusion detection system for Snort to suggest how to develop. The public can use hacking tools and conduct a mock hacking, Snort detects an attack of hacking tools to verify from experimental verification of the suitability of the thesis throughout.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.129-137
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• 2004

This paper presents a signature verification method that uses Gabor filter in computing similarity between signatures. In computing similarity to compare two on-line signatures, the temporal relationship between two signatures should be computed in advance. However, conventional point matching method using DP(dynamic programming) matching consumes much computation. In this paper, we propose a fast method for computing the temporal relationship between two on-line signatures by using the phase output of Gabor Inter applied on the on-line signature signals. Two similarity measures are defined in the method: Temporal Similarity and Temporally Arranged Feature Profile Similarity. With the proposed method, Ive could compare signatures 30 times faster than conventional method using DP matching.