• Journal of Integrative Natural Science
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• v.9 no.2
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• pp.155-160
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• 2016

Applying image processing techniques to education, the face of the learner is photographed, and expression and movement are detected from video, and the system which estimates degree of concentration of the learner is developed. For one learner, the measuring system is designed in terms of estimating a degree of concentration from direction of line of learner's sight and condition of the eye. In case of multiple learners, it must need to measure each concentration level of all learners in the classroom. But it is inefficient because one camera per each learner is required. In this paper, position in the face region is estimated from video which photographs the learner in the class by the difference between frames within the motion direction. And the system which detects the face direction by the face part detection by template matching is proposed. From the result of the difference between frames in the first image of the video, frontal face detection by Viola-Jones method is performed. Also the direction of the motion which arose in the face region is estimated with the migration length and the face region is tracked. Then the face parts are detected to tracking. Finally, the direction of the face is estimated from the result of face tracking and face parts detection.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.223-226
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• 2003

When we detect side-view face in color image, we decide a candidate face region using skin-tone color, and confirm to the face by template matching. Cang Wei use a left and a right template of face, calculate to similarity value by hausdorff method, and decide the final side-view face. It has a characteristic that side-view face is wide spreading neck region. To get exactly result, face region is separated vertically by 3 pixel unit, and matched template. In this paper, we assume that a side-view face is a right side-view or a left side-view face. We separate a half of the candidate face region vertically, and regard a left side as left candidate face, a right side as right candidate face by template matching. This method detect faster than Gang Wei method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.81-96
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• 2024

This study analyzed the differences in the effects on entrepreneurship and entrepreneurial willingness of college students under the coronavirus pandemic by dividing theoretical education into practical education, face-to-face education, and non-face-to-face education, and analyzed the differences in the effects on entrepreneurship and entrepreneurship willingness according to the education method. This study conducted entrepreneurship education for 552 students at a comprehensive university in Chungcheong-do, Korea, and analyzed the sample by dividing it into theoretical and practical education, face-to-face education, and non-face-to-face education. In addition, a two-way repeated measures ANOVA was conducted to determine whether there were differences in the entrepreneurship education course operation form according to the pre- and post-education time points. The results showed that, first, the difference between the effectiveness of entrepreneurship education before and after theoretical and practical education was significant, and the entrepreneurship of practical education was higher than that of theoretical education after education. In the test of pre- and post-training differences in entrepreneurial intention, the difference in effectiveness was significant only in practical training. Second, the results of the repeated measures ANOVA analysis of the course operation type of theoretical and practical courses according to the difference between the pre- and post-education time points showed that there were differences in the entrepreneurship effectiveness of theoretical and practical courses according to the time point of education. Third, the difference in the effectiveness of entrepreneurship education according to face-to-face and non-face-to-face education was significant, and only the effect of non-face-to-face education on entrepreneurial intention was significant before and after education. Fourth, the results of repeated measures ANOVA analysis of face-to-face and non-face-to-face course operation type showed that the effect of face-to-face and non-face-to-face entrepreneurship education differed depending on the time of education. The pre-post difference in entrepreneurial intention was significant only for the non-face-to-face program. The implication of this study is that in order to increase the effectiveness of entrepreneurship and entrepreneurial will among university students, it is necessary to expand the amount of practical classes in which students actively participate in activities related to entrepreneurship. In addition, in order to increase the effectiveness of entrepreneurial will, a non-face-to-face education method that utilizes the metaverse space and increases the role of each student can contribute to increasing the effectiveness of entrepreneurial will.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• 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 International Journal of Costume Culture
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• v.6 no.2
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• pp.93-104
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• 2003

The purpose of this study was to investigate the relationship between women's face satisfaction and makeup satisfaction, to disclose the differences of makeup satisfaction according to demographic variables, and to examine how makeup satisfaction was influenced by face satisfaction and demographic variables. The subjects were 200 women over age 17 living in Seoul and its peripheral areas. The results of this study were as follows: Face satisfaction were drawn three factors. Factor 1 was face contour satisfaction, Factor 2 was skin satisfaction, and Factor 3 was lips and eyes satisfaction. There were significant positive relationship between factors of face satisfaction and makeup satisfaction. Also, the face contour satisfaction was in positive correlation with satisfaction of features, and the skin satisfaction was in positive correlation with that of features. There were significant positive correlations between makeup satisfaction and face shape, eyes, nose, lips, chin, and cheek bone satisfaction. Face satisfaction didn't show significant difference according to demographic variables, but makeup satisfaction showed significant difference according to age and occupation. Face satisfaction was influenced by the facial face, clarity of skin, elasticity of skin, skin color, and ages. The explanatory power of the 4 variables were 24.5%. Makeup satisfaction was influenced by lips and eyes satisfaction, ages, and skin care level. The explanatory power of the 3 variables were 13.3%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.176-191
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• 2021

Non-face-to-face lectures have become a necessity rather than an option since COVID-19, and in order to improve the quality of university education, it is necessary to explore the properties of non-face-to-face lectures and make active efforts to improve them. This study, focusing on this, aims to provide basic data necessary for decision-making for non-face-to-face lecture design by analyzing the relative importance and execution satisfaction of non-face-to-face lecture attributes for professors and students. Based on previous research, a questionnaire was constructed by deriving 4 factors from 1st layer and 17 from 2nd layer attributes of non-face-to-face lectures. A total of 180 valid samples were used for analysis, including 60 professors and 120 students. The importance of the non-face-to-face lecture properties was calculated by obtaining the weights for each stratified element through AHP(Analytic Hierachy Process) analysis, and performance satisfaction was calculated through statistical analysis based on the Likert 5-point scale. As a result of the AHP analysis, both the professor group and the student group had the same priority for the first tier factors, but there was a difference in the priorities between the second tier factors, so it seems necessary to discuss this. As a result of the IPA(Importance Performance Analysis) analysis, the professor group selected the level of interaction as an area to focus on, and it was confirmed that research and investment in teaching methods for smooth interaction are necessary. The student group was able to confirm that it is urgent to improve and invest in the current situation so that the system can be operated stably by selecting the system stability. This study uses AHP analysis for professors and students groups to derive relative importance and priority, and calculates the IPA matrix using IPA analysis to establish the basis for decision-making on future face-to-face and non-face-to-face lecture design and revision. It is meaningful that it was presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.499-505
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• 2003

This paper proposes methods to extract face elements and facial characteristics points for face recognition. We select a candidate region of the face elements with geometrical information between them inside the extracted face region with skin color and extract them using their inherent features. The facial characteristics to be applied to face recognition is expressed with geometrical relation such as distance and angle between the extracted face elements. Experiment results shows good performance to extract of face elements.

• Journal of Science Education
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• v.46 no.1
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• pp.40-52
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• 2022

Daily science classes, which have been continued as part of the spread of participatory science culture, has taken the lead in popularizing science as an effective out-of-school experiential and research activity. However, due to the recent COVID-19 situation, daily science classes have also become an environment in which there is no choice but to switch to non-face-to-face or to combine face-to-face and non-face-to-face education. Therefore, in this study, we examine how elementary school students participating in the non-face-to-face daily science class program change their usual fields of interest, perception of science and technology, interest about science, and scientific competency. In addition, the educational effectiveness of the non-face-to-face daily science class improved by comparing the differences in perceptions of students and parents, and future operation plans were sought. As a result of the study, after participating in the non-face-to-face daily science class program, students' interest in science and technology development, future technology, environmental pollution, and social media increased, and their interest in games decreased. Also, students' interest in science and technology activities, interest in science, and scientific competency also increased. This shows that non-face-to-face daily science class education is effective. Therefore, it was suggested that it is necessary to diversify the learning topics and content levels of the daily science class program, to expand the opportunities of non-face-to-face science education for underprivileged learners, and to develop and share science content using the latest media.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.571-576
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• 2016

3D printing has recently been used in various fields. Among various applications, 3D face data must be generated for 3D face printing. A laser scanner is used to acquire 3D face data, but there is a restriction that a person should not move during scanning. In this paper, we propose a 3D face modeling method based on a single image and a face transformation system to use the generated 3D face for virtual cosmetic surgery. We have defined facial feature points from the 3D face database for 3D face data generation. After extracting feature points from a single face image, 3D face of the input face image is generated corresponding to the 3D face feature points defined from the 3D face database. After 3D face modeling, 3D face modification part is applied for use such as virtual cosmetic surgery.