• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.167-175
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• 2023

In the context of COVID-19, this study was conducted to study how college students' earned grades and average grade point averages changed according to face-to-face and non-face-to-face classes. For this study, grade data was extracted using an access database. For the study, 152 students during the 3rd semester were compared and analyzed the grade point average, average grade point average, midterm exam, final exam, assignment score, and attendance score of students who participated in non-face-to-face and face-to-face classes. As an analysis method, independent sample t-test statistical processing was performed. It was concluded that the face-to-face class students had better grades and average GPA. As a result, the face-to-face class students showed 4.39 points higher than the non-face-to-face class students, and the average grade value was 0.6642 points higher. As a result of the comparative analysis, it was statistically significant, and the face-to-face class averaged 21.22 and the non-face-to-face class had 16.83 points. In conclusion, it was confirmed that face-to-face students' grades were generally higher than those of non-face-to-face students, and that face-to-face students showed higher participation in class.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.226-236
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• 2022

We compared and analyzed the changes in students' science grades and their effects on science grades by level (upper, middle, and lower) according to non-face-to-face and face-to-face teaching-learning. 66 students from A Middle School in Gyeongsangnam-do were selected for the study. As a result of analyzing the change in science grades according to the teaching-learning type, the average score of science grades by non-face-to-face teaching-learning was lower than the corresponding score of science grades of face-to-face teaching-learning. As a result of comparing the level of understanding of learning content according to the evaluation type (paper-written, study-paper) in non-face-to-face and face-to-face teaching-learning, the average scores of science grades by paper-written and study-paper evaluations in non-face-to-face teaching-learning were significantly low. In addition, as a result of comparing the effect on science grades by level according to the teaching-learning type, the average score of science grades of lower-ranked students in non-face-to-face teaching-learning was relatively low.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.237-244
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• 2024

We studied the effects on their academic achievement of chemistry II and advanced chemistry subjects of science high school students according to the type of class (face-to-face and non-face-to-face). The subjects of this study were 195 first-year students of G Science High School located in Gyeongnam. The average scores of Chemistry II and Advanced Chemistry in non-face-to-face classes in 2020 and face-to-face classes in 2021 were compared and analyzed. As a result of comparing and analyzing the academic achievement according to the class type, students' grades in Chemistry II and Advanced Chemistry were higher in non-face-to-face classes. In the comparison of academic achievement by level according to class type, Chemistry II showed higher average grades in non-face-to-face classes as the lower level were, and in advanced chemistry, the higher the upper grades in non-face-to-face classes. In addition, in terms of the effect of changes in class form on the upper and lower 10% levels of academic achievement of Chemistry II, the upper 10% showed high grades in face-to-face classes and the lower 10% in non-face-to-face classes. On the other hand, in advanced chemistry, the average grade of non-face-to-face classes was higher than that of face-to-face classes in the top 10%, and the average grade of face-to-face classes was higher than that of non-face-to-face classes in the bottom 10%. Through these results, it was found that in the teaching-learning of science high school students, instructors need to design and treat teaching-learning appropriate to the level of academic achievement.

• 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.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.211-224
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• 2023

This study surveyed 2nd graders of B high school and 1st graders of A university in Gyeongnam on factors such as behavior control and interaction in non-face-to-face classes, easy or difficult concepts presented in chemistry I and general chemistry textbooks. Based on the results of the survey, the effect of changes in the difficulty level of concepts presented in chemistry I and general chemistry and changes in class types (face-to-face and non-face-to-face) on students' academic achievement by level was compared and analyzed. In the face-to-face class, the average score between the first and second semesters was similar according to the change in the difficulty of the concepts presented in chemistry I and general chemistry. In the non-face-to-face class, the average score of chemistry I in the second semester was quite low, and the average score of general chemistry was rather high. In non-face-to-face classes, the average score of chemistry I in the second semester of low-level students was significantly lowered due to changes in the difficulty of the concept and changes in class types on academic achievement by level. In the case of 10% of students at the lower level, the academic achievement of chemistry I decreased in both the second semester regardless of the changes in the difficulty level of concepts and the changes in class types.

• 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.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.6
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• pp.938-948
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• 2009

The purpose of this study was to offer typical facial shapes Korean women in their 20's to 50's. We used facial photographs of 600 Korean women obtained from $2003\sim2004$ Size Korea Project and we measured these photographs indirectly in this study by utilizing the Venus face2D program. Total 62 measurements on the face were measured and analyzed by statistical methods. The results were as follows. First of all, the mean of face length was 196mm, top face length was 62.3mm, middle face length was 68.9mm, bottom face length was 66.5mm, mean of forehead width was 125.1mm. As based on those average sizes, we proposed a average facial size and shape of Korean women and a average facial size and shape of 20's, 30's, 40's and 50's in this study. When examined characteristic of 20's facial shape, it was recognized that the width of forehead was wider and the width of gnathion was smaller than other age groups. In the characteristic of 30's facial shape, the ratios of facial length, top of face, middle of face and bottom of face were balanced well, as comparing with other age groups. Overall, the values of facial measurement of 30's were similar to the averages of total women. In the facial shape of 40's, mean length and width of face each were the smallest among each age group. The eye shape of 40's was more drooped than the average eye shape and the protrusion of the zygomatic bone was significantly different. In case of the facial shape of 50's, it was similar to the facial shape of 40's, but mean lengths and widths of 50's face were slightly larger than the values of 40's. The eye shape of 50's was more drooped than average group and the eye length was the smallest among all age groups.

• Journal of Sasang Constitutional Medicine
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• v.19 no.2
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• pp.30-39
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• 2007

1. Objectives The Face is an important standard for the classification of Sasang Constitution. We are developing 3D Automatic Face Recognition Apparatus(3D-AFRA) to analyse the facial characteristics. This apparatus show us 3D image and data of man's face and measure facial figure data. So we should examine the figure restoration error of 3D Automatic Fare Recognition Apparatus(3D-AFRA) in hardware Error Analysis. 2. Methods We scanned Face status by using 3D Automatic Face Recognition Apparatus(3D-AFRA). And also we scanned Face status by using laser scanner(vivid 9i). We compared facial shape data be restored by 3D Automatic Face Recognition Apparatus(3D-AFRA) with facial shape data that be restorated by 3D laser scanner. And we analysed the average error and the maximum error of two data. 3. Results and Conclusions In frontal face, the average error was 0.48mm. and the maximum error was 4.60mm. In whole face, the average error of was 0.99mm. And the maximum error was 6.64mm. In conclusion, We assessed that accuracy of 3D Automatic Face Recognition Apparatus(3D-AFRA) is considerably good.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4080-4097
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• 2020

In recent years, convolutional neural network (CNN) has become the primary method for face detection. But its shortcomings are obvious, such as expensive calculation, heavy model, etc. This makes CNN difficult to use on the mobile devices which have limited computing and storage capabilities. Therefore, the design of lightweight CNN for face detection is becoming more and more important with the popularity of smartphones and mobile Internet. Based on the CPU real-time face detector FaceBoxes, we propose a multi-task lightweight face detector, which has low computing cost and higher detection precision. First, to improve the detection capability, the squeeze and excitation modules are used to extract attention between channels. Then, the textual and semantic information are extracted by shallow networks and deep networks respectively to get rich features. Finally, the landmark detection module is used to improve the detection performance for small faces and provide landmark data for face alignment. Experiments on AFW, FDDB, PASCAL, and WIDER FACE datasets show that our algorithm has achieved significant improvement in the mean average precision. Especially, on the WIDER FACE hard validation set, our algorithm outperforms the mean average precision of FaceBoxes by 7.2%. For VGA-resolution images, the running speed of our algorithm can reach 23FPS on a CPU device.

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.24-31
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• 2017

Face-based video retrieval has become an active and important branch of intelligent video analysis. Face profiling and matching is a fundamental step and is crucial to the effectiveness of video retrieval. Although many algorithms have been developed for processing static face images, their effectiveness in face-based video retrieval is still unknown, simply because videos have different resolutions, faces vary in scale, and different lighting conditions and angles are used. In this paper, we combined content-based and semantic-based image analysis techniques, and systematically evaluated four mainstream local features to represent face images in the video retrieval task: Harris operators, SIFT and SURF descriptors, and eigenfaces. Results of ten independent runs of 10-fold cross-validation on datasets consisting of TED (Technology Entertainment Design) talk videos showed the effectiveness of our approach, where the SIFT descriptors achieved an average F-score of 0.725 in video retrieval and thus were the most effective, while the SURF descriptors were computed in 0.3 seconds per image on average and were the most efficient in most cases.