• Journal of Multimedia Information System
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• v.6 no.2
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• pp.91-98
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• 2019

Many researchers have attempted to recognize three-dimensional faces using feature points extracted from two-dimensional facial photographs. However, due to the limit of flat photographs, it is very difficult to recognize faces rotated more than 15 degrees from original feature points extracted from the photographs. As such, it is difficult to create an algorithm to recognize faces in multiple angles. In this paper, it is proposed a new algorithm to recognize three-dimensional face recognition based on feature points extracted from a flat photograph. This method divides into six feature point vector zones on the face. Then, the vector value is compressed and expanded according to the rotation angle of the face to recognize the feature points of the face in a three-dimensional form. For this purpose, the average of the compressibility and the expansion rate of the face data of 100 persons by angle and face zone were obtained, and the face angle was estimated by calculating the distance between the middle of the forehead and the tail of the eye. As a result, very improved recognition performance was obtained at 30 degrees of rotated face angle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2547-2567
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• 2021

It has been widely acknowledged that occlusion impairments adversely distress many face recognition algorithms' performance. Therefore, it is crucial to solving the problem of face image occlusion in face recognition. To solve the image occlusion problem in face recognition, this paper aims to automatically de-occlude the human face majority or discriminative regions to improve face recognition performance. To achieve this, we decompose the generative process into two key stages and employ a separate generative adversarial network (GAN)-based network in both stages. The first stage generates an initial coarse face image without an occlusion mask. The second stage refines the result from the first stage by forcing it closer to real face images or ground truth. To increase the performance and minimize the artifacts in the generated result, a new refine loss (e.g., reconstruction loss, perceptual loss, and adversarial loss) is used to determine all differences between the generated de-occluded face image and ground truth. Furthermore, we build occluded face images and corresponding occlusion-free face images dataset. We trained our model on this new dataset and later tested it on real-world face images. The experiment results (qualitative and quantitative) and the comparative study confirm the robustness and effectiveness of the proposed work in removing challenging occlusion masks with various structures, sizes, shapes, types, and positions.

• Journal of the Korean Society of Radiology
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• v.14 no.7
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• pp.923-930
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• 2020

The COVID-19 crisis inevitably led to full-scale non-face-to-face education in 2020. The researchers selected five universities out of 48 universities in radiology and radiology departments nationwide (1.2 in 2019 and 1.2 in 2020) to examine the results of face-to-face training and non-face-to-face mock tests conducted by senior students in radiology departments and radiology departments of the national health department (12 in 2019 and 1.2 in 2020) in preparation for the license test. It turned out to be. Comparing the results of face-to-face and non-face-to-face training (two in 2019 and two in 2020), statistics were analyzed by nonparametric tests, and the results showed that there were differences in theory (Z=-2.023, p<0.05, application (Z=-2.023), p<0.05), practical skills (Z=-1.753, and p<0.05). As a result of the mock test, non-face-to-face education results in poor grades compared to face-to-face education, it is believed that lectures should be taught differently or various educational methods that can communicate with students should be combined.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.45-50
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• 2021

In order for SW education to be established in the era of non-face-to-face caused by COVID-19, research on the efficiency of SW education according to face-to-face and non-face classes is needed. Therefore, this study classified the operation status of the curriculum of 30 SW gifted classes nationwide in 2020 according to the class method(face-to-face, non-face, and blended). Subsequently, the results of class time and production per person were compared and analyzed through quantitative analysis. According to the study, the type of classes that performed the most classes compared to the planned number of hours was non-face-to-face(90.9%), followed by face-to-face(84.2%) and the least was blended(80.5%). The average number of products per student was the highest in the face-to-face class(0.504), while the blended class(0.421) and non-face-to-face class(0.42). Based on the results of this study, the non-face-to-face approach is advantageous in securing the number of hours, but various measures should be prepared to solve this problem because teachers and students find it difficult to guide the output.

• Journal of the Korea Society for Simulation
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• v.20 no.4
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• pp.13-20
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• 2011

The face recognition has been used in a variety fields, such as identification and security. The procedure of the face recognition is as follows; extracting face features of face images, learning the extracted face features, and selecting some features among all extracted face features. The selected features have discrimination and are used for face recognition. However, there are numerous face features extracted from face images. If a face recognition system uses all extracted features, a high computing time is required for learning face features and the efficiency of computing resources decreases. To solve this problem, many researchers have proposed various Boosting methods, which improve the performance of learning algorithms. Mutual-Boost is the typical Boosting method and efficiently selects face features by using mutual information between two features. In this paper, we propose a GroupMutual-Boost method for improving Mutual-Boost. Our proposed method can shorten the time required for learning and recognizing face features and use computing resources more effectively since the method does not learn individual features but a feature group.

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.31-37
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• 2021

This study aims to develop a face-to-face & non-face-to-face convergence tea culture therapy program for elderly people experiencing CORONA 19 pandemic anxiety and digital alienation, and apply the program to eight senior citizens aged 70 or older who use the K Senior Citizens' Day Care Center in P City to verify its effectiveness. Anxiety among elderly people experiencing coronavirus anxiety was 3.02 (SD 0.25) before participating in the program and 2.79 (SD 0.15), indicating a significant difference between before and after participating in the program (Z=4.245, P=.004) Based on this analysis, we present practical suggestions for the expansion of face-to-face & non-face-to-face convergence tea culture therapy programs to alleviate anxiety among elderly people who experience CORONA 19 pandemic anxiety.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.283-292
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• 2021

As universities have operated non-face-to-face semesters due to COVID-19, although instructors applying flipped learning to their classes also have changed it into non-face-to-face ways, there is still a lack of exploratory research on effectiveness of the new form of flipped learning. In this study, we explored the effectiveness of the non-face-to-face flipped learning by analyzing students' learning experiences throughout FGI and survey. By doing so, we sought to provide in-depth insights for successful implications of non-face-to-face flipped learning classes ultimately. The findings showed that many learners positively evaluated non-face-to-face flipped learning in terms of interactions, including quizzes, team activities, and interpersonal interactions (e.g., Q&A, feedback) with professors in non-face-to-face flipped learning classes. The result of the survey also showed significant differences in the pre-post test regarding learner's perceived learning achievement. Based on these findings, the implications were discussed.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.404-413
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• 2022

As non-face-to-face classes have been adopted as an essential class method in universities after COVID-19, interest in ways to encourage student engagement is increasing. Class engagement is a prerequisite for improving the quality of education, so it is inevitably an even more important requirement in non-face-to-face classes. Therefore, this study examined the efforts and concerns based on the teaching experiences of three professors of D University, which have been operated by mixing non-face-to-face or non-face-to-face classes since 2020. As a result, both professors and students went through trial and error in the early stages of non-face-to-face classes, but over time, it was confirmed that students not only actively expressed their opinions but also voluntarily expanded the class activity. This study is meaningful in that it found the possibility that professors-led classes can develop into learner-participating classes through appropriate harmony between face-to-face and non-face-to-face and the use of various media. Data were collected through an autobiographical method.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.613-621
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• 2022

The purpose of this study is to understand the transition experience from face-to-face lectures by nursing professors to non-face-to-face lectures. In this study, 17 nursing professors who have conducted non-face-to-face lectures for at least two semesters were collected through in-depth individual interviews according to the empirical phenomenological research method and analyzed according to the qualitative topic analysis method. The research results described the transition experiences of non-face-to-face lectures, focusing on the topics of change and development process according to non-face-to-face lectures, relationship with students, self-relationship, social context and sociality. In conclusion, nursing professors are expected to receive sufficient support to autonomously select non-face-to-face lectures using advanced technologies according to the trend of social change.