• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.305-320
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• 2021

Due to COVID-19, the education and training environment in vocational competency development has changed significantly. In vocational education and training, where the proportion of face-to-face training is more extensive than in other areas of education, some training courses had no choice but to be converted to online. This study presents a distance training system plan for non-contact vocational training by analyzing the learner's non-contact learning experiences. Non-face-to-face education experiences were investigated for learners of private vocational training institutions, universities, and public higher vocational training institutions. The main contents of the survey were to analyze the non-face-to-face learning experiences of these learners for the educational environment and educational purposes. Based on the results of the learners' non-face-to-face learning experiences, a draft of a remote training system construction plan for non-face-to-face education was composed, and a Delphi study was conducted on the draft non-face-to-face remote training system. A method for establishing a distance training system including non-face-to-face teaching and learning strategies, learning and operation support was proposed with these results.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.11-20
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• 2010

In this paper we propose a face detection system which consists of a method of face candidate extraction using skin color and a method of face verification using the feature of facial structure. Firstly, the proposed extraction method of face candidate uses the image segmentation and merging algorithm in the regions of skin color and the neighboring regions of skin color. These two algorithms make it possible to select the face candidates from the variety of faces in the image with complicated backgrounds. Secondly, by using the partial face classifier, the proposed face validation method verifies the feature of face structure and then classifies face and non-face. This classifier uses face images only in the learning process and does not consider non-face images in order to use less number of training images. In the experimental, the proposed method of face candidate extraction can find more 9.55% faces on average as face candidates than other methods. Also in the experiment of face and non-face classification, the proposed face validation method obtains the face classification rate on the average 4.97% higher than other face/non-face classifiers when the non-face classification rate is about 99%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4835-4848
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• 2020

The recent expansion of the digital content market is increasing the technical demand for various facial image transformations within the virtual environment. The recent image translation technology enables changes between various domains. However, current image-to-image translation techniques do not provide stable performance through unsupervised learning, especially for shape learning in the face transition field. This is because the face is a highly sensitive feature, and the quality of the resulting image is significantly affected, especially if the transitions in the eyes, nose, and mouth are not effectively performed. We herein propose a new unsupervised method that can transform an in-wild face image into another face style through radical transformation. Specifically, the proposed method applies two face-specific feature loss functions for a generative adversarial network. The proposed technique shows that stable domain conversion to other domains is possible while maintaining the image characteristics in the eyes, nose, and mouth.

• Journal of Korean Clinical Health Science
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• v.10 no.1
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• pp.1543-1552
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• 2022

Purpose: It is intended to investigate the satisfaction of dental hygiene students with non-face-to-face online classes and use them as basic data for successful lecture design and operation. Methods: The data collected in this study were analyzed using the lBM SPSS Statistics 21 program. The general characteristics of the study subjects were frequency analysis, non-face-to-face online class satisfaction, and test satisfaction were frequency analysis and technical statistics. Through the independent sample T test, a t-test was conducted to find out whether there was an average difference in online class and test satisfaction according to grade. Results: The advantages of non-face-to-face online classes were that repetitive learning was possible (57.7%), the disadvantage was that there was a lack of real-time communication (74.9%), and the most efficient teaching method was a mixed form of online and face-to-face classes (64.9%). The satisfaction level of online classes was 2.69 points for 'self-directed learning habits,' which was the highest compared to the overall average of 2.55 points, and 2.09 points for 'difficulty in interaction between instructors and learners in online classes.'Non-face-to-face test satisfaction was 2.68 points for 'short test time gives fairness to test results,' higher than the overall average of 2.45 points, and 2.07 points for 'no difficulty accessing the test.'In terms of satisfaction with the non-face-to-face test according to the grade, it was found that the third grade showed a more negative attitude than the second grade in terms of sexual fairness (p<0.05). Conclusions: Through the above results, non-face-to-face online classes require various content development and some mixed classes considering the level of students, and instructors' efforts to improve the quality of classes for interaction between instructors and learners are needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1705-1720
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• 2013

Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.

• Journal of Information Technology Applications and Management
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• v.29 no.1
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• pp.29-45
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• 2022

The Fourth Industrial Revolution era requires people to have the ability of integrated thinking, critics, sensitivity, and creativity in an integrated manner. Therefore, teaching methods are expected to become more suitable for the trend. In this belief, current teacher-leading education method should move to students' self motivating one and consist of programs in which students voluntarily involve. In this reason, this study suggests FPBL educational method model that is combines project-based learning with flipped learning by analysing preceding research and digital painting tool class was designed by applying it. As a result of applying the designed class model to the class, all of the class satisfaction, effectiveness, and interaction were evaluated positively. Problems such as limitations of project classes due to non-face-to-face classes, large amount of learning before class, and reduced concentration during class were found. Therefore, when the FPBL class model is conducted non-face-to-face, it will be necessary to further strengthen the role of the instructor, provide lecture videos summarizing the core contents, and improve concentration by providing active participation and fun using various digital tools. The result of the study looks significant by confirming the possibility of applying FPBL model not only in design education but also other educational settings.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.175-181
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• 2021

The purpose of this study is to improve and develop the quality of non-face-to-face classes according to the types of presence by analyzing the effects of teaching presence and learning presence on the learning satisfaction of the non-face-to-face classes that have been suddenly conducted due to COVID-19. For this purpose, a survey on online classes of H University in Gwangju Metropolitan City was conducted to analyze learning satisfaction, teaching presence (learning design, direct promotion), and learning presence (cognitive presence, social presence). The results of the analysis showed that the learning contents of cognitive presence, which is a sub-factor of learning presence, were understood (=.589, p<.001), the direct promotion (=.420, p<.001), and the learning design (=.397, p<.01), which are the sub-factors of teaching presence, were influential in order.This means that the suddenly changed teaching method should have an attitude to improve the intimacy between the instructor and the fellow learners with positive emotional exchange or interaction. The instructor should try to overcome the limitations of time and space through blended learning that is both online and offline for high quality learning design, but the learning medium and learning method considering the physical fatigue of the learner should be developed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.503-523
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• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.

• Journal of Engineering Education Research
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• v.23 no.2
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• pp.24-30
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• 2020

This paper proposes the approaches to the evaluation of learning using concepts of artificial intelligence. Among various techniques, deep learning algorithm is employed to achieve quantitative results of evaluation. In particular, this paper focuses on the process-based evaluation instead of the result-based one using face expression. The expression is simply acquired by digital camera that records face expression when students solve sample test problems. Face expressions are trained using convolutional neural network (CNN) model followed by classification of expression data into three categories, i.e., easy, neutral, difficult. To substantiate the proposed approach, the simulation results show promising results, and this work is expected to open opportunities for intelligent evaluation system in the future.

• The Journal of Korean Society for School & Community Health Education
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• v.12 no.3
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• pp.17-28
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• 2011

Backgroud & Objectives: The purpose of the study was to develop an innovative blended learning method on life style and health promotion and evaluate the educational effects for university students. Methods: The blended learning was developed to combine face-to-face lecture(off-line lecture) and on-line lecture that applied the subject of life style and health promotion. This course is a coordinated effort towards providing 5 topics of lifestyle such as smoking, alcohol, exercise, diet, and stress management. This has been verified by an expert in the field of nursing, education, e-learning technician and students. Participants were different part of university students (n=28) with major enrolled in a general culture course for 2 credits which composed of 8 sessions of each 2-hour in the first semester of 2010. The study was a one group posttest design. A self-report about health knowledge, attitude, and health behavior was organized by content analysis after the sessions. Results: Positive feedbacks from students were reflected in the outcome. Student regarded good lifestyle as being the most important. Student concerned those on-line lectures are not only available at most time and site, but also good for individualization, visual understanding and interest. Face-to-face lecture provided student a chance to integrate with knowledge and experience and had desire to improve good lifestyle and health promotion. Conclusions: The blended learning method on good lifestyle and health could make a best use of improvement for knowledge, attitude and behavior concerning. It is needed to identify the long term effects of a blended learning for further study.