• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.341-348
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• 2021

With the advent of the pandemic era, non-face-to-face classes are being conducted, and many studies are being conducted on the effects of non-face-to-face classes in many subjects. Since the programming class is a subject that combines knowledge transfer and practice. In the case of non-face-to-face classes, I wanted to know how students perceive them through a survey. The number of students who answered that traditional face-to-face classes were good in terms of ease of question and answer, understanding of class content and immersion in class, and more students answered that non-face-to-face classes were good in terms of securing practice time and class control. It is expected that using the results of this study will help plan effective programming lessons.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.10-17
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• 2017

This study is the first attempt to raise face recognition ability through machine learning algorithm and apply to CRM's information gathering, analysis and application. In other words, through face recognition of VIP customer in distribution field, we can proceed more prompt and subdivided customized services. The interest in machine learning, which is used to implement artificial intelligence, has increased, and it has become an age to automate it by using machine learning beyond the way that a person directly models an object recognition process. Among them, Deep Learning is evaluated as an advanced technology that shows amazing performance in various fields, and is applied to various fields of image recognition. Face recognition, which is widely used in real life, has been developed to recognize criminals' faces and catch criminals. In this study, two image analysis models, TF-SLIM and Inception-V3, which are likely to be used for criminal face recognition, were selected, analyzed, and implemented. As an evaluation criterion, the image recognition model was evaluated based on the accuracy of the face recognition program which is already being commercialized. In this experiment, it was evaluated that the recognition accuracy was good when the accuracy of the image classification was more than 90%. A limit of our study which is a way to raise face recognition is left as a further research subjects.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.289-297
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• 2022

The purpose of this study was to investigate the perception of dental students based on their experiences of online classes after taking non-face-to-face education courses for all the school semesters in 2020. For the research method, an online survey was conducted on A survey was conducted on 161 dental students enrolled in A University. The analytical method was conducted through frequency analysis, correlation analysis, and multiple regression analysis. The survey analysis findings showed that the satisfaction of dental students' about the non-face-to-face education course was above 4.2, and the detailed items were in the order of the appropriateness of the attendance processing method, satisfaction with recorded video lectures, and the assessment method of the course grade. In the case of the factors that affect the satisfaction of non-face-to-face education courses, the learning system and assessment method were statistically significant. The online class type that is most preferred by the students is recorded video lectures, and the highest number of participants chose 21~30 minutes as the appropriate time for the class content. It is considered that the application of the online system will continue to be used together with face-to-face education courses in the education site and various university-level efforts like systematic support are required to achieve effective learning achievements. This study only investigated the non-face-to-face education operation conditions of A University, so it cannot be generalized to all universities, but it can be used as basic data to provide education curriculum design and supportive measures for the compatibility of face-to-face and non-face-to-face courses.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.169-176
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• 2024

The purpose of this study is a qualitative study to understand the meaning and essence of nursing students' non-face-to-face learning experience. It was conducted for two months from March to April 2022, targeting 12 students who participated in non-face-to-face learning, and three focus groups were formed and interviewed. Interview data were analyzed using Coaizzi's phenomenological research method. As a result of the study, 4 themes and 12 subtopics were derived. The four themes were 'freedom', 'efficiency', 'self-control', and 'lack of social skills'. Nursing students learned the meaning of non-face-to-face learning through non-face-to-face learning. Through the non-face-to-face learning experience of nursing students, the true meaning of non-face-to-face learning was analyzed and learned. The results of this study provided an understanding of the non-face-to-face learning experience of nursing students, which can be applied to the diversification of non-face-to-face education programs.

• Journal of Internet Computing and Services
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• v.5 no.1
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• pp.59-66
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• 2004

Symmetry region searching can extract face region without a prior information in an image by using symmetric. However, this method requires a plenty of the computation time because the mask size to process symmetry region searching must be larger than the size of object such as eye, nose and mouth in face. in this paper, it proposed symmetric by using symmetry region searching and Wavelet Transform to reduce computation time of symmetry region searching, and It was applied to this method in an original image. To extract exact face region, we also experimented face region searching by using domain division in extraction region.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.844-851
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• 2019

In this paper, we propose a loss function in CNN that introduces inter-class amplitudes to increase inter-class loss and reduce intra-class loss to increase of face recognition performance. This loss function increases the distance between the classes and decreases the distance in the class, thereby improving the performance of the face recognition finally. It is confirmed that the accuracy of face recognition for visible light image of proposed loss function is 99.62%, which is better than other loss functions. We also applied it to face recognition of visible and near-infrared complex images to obtain satisfactory results of 99.76%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1012-1020
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• 2000

In this paper, This paper presents a new algorithm which is used for detecting and extracting human masks from a color still image. The regions where each pixel has a value of skin-color were extracted from the Cb and Cr images, after the tone of the color image is converted to YCbCr from. A morphological filter is used to eliminate noise in the resulting image. By scanning it in horizontal and vertical ways under ways under threshold value, first candidate section is chosen. If it is not a face, secondary candidate section is taken and is divided into two candidate sections. The proposed algorithm is not affected by the variation of illuminations, because it uses only Cb and Cr components in YCbCr color format. Moreover, the face recognition was possible regardless of the degree of shifting face, changed shape, various sizes of the face, and the quality of image.

• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.376-388
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• 2023

After a longitudinal analysis of the data on the college entrance examination of students enrolled in the Department of Chemistry Education at Gyeongnam from 2014 to 2021, the effect on the academic achievement of general chemistry according to the type of college entrance examination was studied. And the impact on the academic achievement of general chemistry according to the type of admission screening in face-to-face and non-face-to-face teaching-learning was also studied. As a result of analyzing the academic achievement of general chemistry by admission process, students admitted through occasional screening showed relatively high grades of A and B at 88.7%, and the ratio of grades of 1~3 of chemistry I in high school was high. On the other hand, in the case of students admitted through regular admission, the ratio of grades of A and B in general chemistry was very high at 94.3%, and the ratio of grades of 3~4 in chemistry I of the College Scholastic Ability Test was high. As a result of analyzing the academic achievement of general chemistry by class type and admission process, it was found that the grades of chemistry I by face-to-face classes had an effect on the academic achievement of general chemistry in non-face-to-face classes. In both admissions, the academic achievement of general chemistry by face-to-face classes was relatively higher than that of non-face-to-face-to-face classes.

• Journal of Korea Multimedia Society
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• v.7 no.2
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• pp.156-163
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• 2004

In this paper, we propose an adaptive face region detection and real-time face identification algorithm using face feature evaluation function. The proposed algorithm can detect exact face region adaptively by using skin color information for races as well as intensity and elliptical masking method. And also, it improves face recognition efficiency using geometrical face feature and geometric evaluation function between features. The proposed algorithm can be used for the development of biometric and security system areas. In the experiment, the superiority of the proposed method has been tested using real image, the proposed algorithm shows more improved recognition efficiency as well as face region detection efficiency than conventional method.