• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.185-189
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• 2004

For face recognition system, a face detector which can find exact face region from complex image is needed. Many face detection algorithms have been developed under the assumption that background of the source image is quite simple . this means that face region occupy more than a quarter of the area of the source image or the background is one-colored. Color-based face detection is fast but can't be applicable to the images of which the background color is similar to face color. And the algorithm using neural network needs so many non-face data for training and doesn't guarantee general performance. In this paper, A multi-scale, multi-face detection algorithm using PCA is suggested. This algorithm can find most multi-scaled faces contained in static images with small number of training data in reasonable time.

• Korean Educational Research Journal
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• v.43 no.1
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• pp.1-27
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• 2022

As the COVID-19 virus became prevalent worldwide, non-face-to-face contact was implemented in various ways, and the education system also began to draw much attention due to rapid non-face-to-face contact. The purpose of this study is to analyze the direction of non-face-to-face education in line with the continuously changing educational environment to date. In this study, data were visualized using Textom and Ucinet6 analysis tool programs to collect social network big data with various opinions. As a result of the study, keywords related to "COVID-19" were dominant, and keywords with high frequency such as "article" and "news" existed. As a result of the analysis, various issues related to non-face-to-face education, such as network failures and security issues, were identified. After the analysis, the direction of the non-face-to-face education system was studied according to the growth of the education market and changes in the educational environment. In addition, there is a need to strengthen security and feedback on teaching methods in non-face-to-face education analyzed using big data.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.10a
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• pp.185-192
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• 1999

This paper presents the results of a parametric study on the behavior of tunnel face reinforced with horizontal pipes. A three-dimensional finite element model was adopted in this study to capture the three-dimensional nature of tunnel face behavior under various boundary conditions. A parametric study was peformed on a wide range of boundary conditions with emphasis on the effect of reinforcing layouts on the deformation behavior of tunnel face. The results of analysis such as tunnel face deformation behavior under various conditions were thoroughly analyzed, and a database for the behavior of tunnel face under different reinforcing conditions was established for future development of a semi-empirical design/analysis method for the tunnel face reinforcing technique. The results indicated that there exits an optimum reinforcing layout for a given tunnel condition, which must be selected with due consideration of tunnel geometry and ground condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.977-984
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• 2007

A realtime face tracking algorithm using motion analysis from image sequences and color information is proposed. Motion area from the realtime moving images is detected by calculating temporal derivatives first, candidate pixels which represent face region is extracted by the fusion filtering with multiple color models, and realtime face tracking is performed by discriminating face components which includes eyes and lips. We improve the stability of face tracking performance by using template matching with face region in an image sequence and the reference template of face components.

• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.4
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• pp.81-91
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• 2019

The 3-dimensional grain burn-back analysis is performed using the face offsetting method for calculating the solid rocket motor performance. The grain burning configuration analysis is a moving surface problem that calculates the regression of the burning surface. In the previous study, various moving interface analysis methods were applied for the grain burn-back analysis, but the results were imperfect. In this study, a 3-dimensional grain burn-back analysis module is developed using the face offsetting method, which combines the advantages of the existing moving interface analysis methods to increase the accuracy and robustness. As a result, the face offsetting method is proved to be efficient for the grain burn-back analysis.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.831-832
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• 2008

This paper proposes the experimental results and analysis of face recognition on an conventional UMPC(Ultra Mobile Personal Computer). With face images acquired by the embedded camera of UMPC, we detected the facial region by using Adaboost face detector. The detected image was normalized into a $32{\times}32$ pixel sized image for face recognition. We performed face recognition based on PCA (Principal Component Analysis). As experimental results, the TER (Total Error Rate) of face recognition was 19.77%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.5
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• pp.33-40
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• 2004

In this paper, we constructs face database by using wavelet comparison, and compare face recognition rate by using principle component analysis (Principal Component Analysis : PCA) algorithm. General face recognition method constructs database, and do face recognition by using normalized size. Proposed method changes image of normalized size (92${\times}$112) to 1 step, 2 step, 3 steps to wavelet compression and construct database. Input image did compression by wavelet and a face recognition experiment by PCA algorithm. As well as method that is proposed through an experiment reduces existing face image's information, the processing speed improved. Also, original image of proposed method showed recognition rate about 99.05%, 1 step 99.05%, 2 step 98.93%, 3 steps 98.54%, and showed that is possible to do face recognition constructing face database of large quantity.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.737-742
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• 2013

The surveillance system has been developed to be intelligent which can judge and cope by itself using human recognition technique. The existing face recognition is excellent at a short distance but recognition rate is reduced at a long distance. In this paper, we analyze the performance of face recognition according to interpolation and face recognition algorithm in face recognition using the multiple distance face images to training. we use the nearest neighbor, bilinear, bicubic, Lanczos3 interpolations to interpolate face image and PCA and LDA to face recognition. The experimental results show that LDA-based face recognition with bilinear interpolation provides performance in face recognition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Fashion & Textile Research Journal
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• v.6 no.6
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• pp.740-748
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• 2004

The purpose of this research is to observe the face types of types of haircut design that is the basis for the completion of a hair styling mainly with case of male college student. For the purpose of this, I carried out direct and indirect observation and measurement for faces of 293 male college student who attended universities in Busan, and measured face types which were classified by the group analysis preferred hair style in order to do actual hair cut design. The result of this research is as follow. This paper conducted the element analysis in regard to the direct and indirect items of face, and it pulled out 5 elements. As a result of group analysis with elements as independent variables, they are classified into4 types. For actual hair cut design, 4 types classified by group analysis and nearing models analyzed. This research bring accurate information classified face types.