• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.959-975
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• 2013

The SAS macro faces program by Friendly (1992) is for Chernoff face analysis, which is one of methods for the visualization representation of multivariate data. In this paper, we examined 18 face features used in the program and presented the modified program depending on the definition of a good face in days present. In addition, a good bank evaluation for 15 domestic banks was performed through Chernoff face analysis based on 11 bank economic indicators representing stability, the consumer satisfaction, soundness, and banks profitability.

• Journal of Engineering Education Research
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• v.24 no.5
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• pp.53-64
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• 2021

The purpose of this study is to provide implications for designing and implementing non-face-to-face online classes at the College of Engineering in the post-corona era by analyzing the instructors' evaluations and experiences of non-face-to-face online classes operated in the COVID-19 pandemic. According to the overall evaluation results of non-face-to-face online classes from instructors at the College of Engineering, 'instructional design' was the highest among the five areas including instructional design, learning management, learning support, learning evaluation, and instructional outcomes. In addition, the effectiveness of non-face-to-face online experimental or practical classes was found to be relatively low. The results of this study imply that the instructors need to consider several instructional strategies such as active interaction with learners, clear explanation, and the use of technology in non-face-to-face online engineering classes.

• 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.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.21-27
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• 2006

In this paper, object of experiment is to study on the effect parameters to obtain optimal surface roughness in face turning. Surface roughness is significantly important to be high quality of parts produced by turning process. For this purpose, the optimization of cutting parameters for face turning operation is investigated applying the Taguchi method. An orthogonal array, signal-to-noise, and the analysis of variance are employed to evaluate effect of cutting parameters for face turning. Also confirmation tests were performed to make a comparison between the results predicted from the mentioned correlations and the theoretical results. Cutting experiment is performed without cutting fluid using coated tungsten carbide insert about workpiece of SM45C. And regression analysis technique has been used to study the effects of the cutting parameters.

• 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.

• Fashion & Textile Research Journal
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• v.7 no.6
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• pp.650-656
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• 2005

The objective of this study is to analyze the formative attributes of face by measuring the shape and features of face. The faces of women in 20's were taken by digital camera and measured, then it has conducted a statistical analysis using a SPSS for factor analysis, correlation and cluster analysis. The findings are that it is consisted of six(6) different factors and it is responsible for 73.93%. In Factor 1 and Factor 2, it has explained the most significant factor to determine the shape of face. The result on cluster analysis is that it is classified into 5 groups and it is as follows. Attributes of each group is that Group 1 has a wide and long forehead, small and longish chin-line and chubby cheeks that represent polished and modern images, while Group 2 has small and longish forehead and chin-line that represent classical and mature images. On the other hand, Group 3 has a narrow forehead, small and longish chin-line and upward-style eyebrows that represents provocative images, whereas Group 4 has a shaped style that represent intellectual images and Group 5 has small and longish forehead and chin-line and cheekbones that represent polished and cute images.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.41-48
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• 2005

This paper presents a face recognition method based on the combination of well-known statistical representations of Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA) with Radial Basis Function Networks. The original face image is first processed by PCA to reduce the dimension, and thereby avoid the singularity of the within-class scatter matrix in LDA calculation. The result of PCA process is applied to LDA classifier. In the second approach, the LDA process Produce a discriminational features of the face image, which is taken as the input of the Radial Basis Function Network(RBFN). The proposed approaches has been tested on the ORL face database. The experimental results have been demonstrated, and the recognition rate of more than 93.5% has been achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5464-5484
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• 2016

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.

• Educational Technology International
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• v.8 no.2
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• pp.53-71
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• 2007

This study identified the effectiveness of e-learning by comparing learning outcome in conventional face-to-face lecture with the selected e-learning methods. Two e-learning contents (animation based and video based) were developed based on the rapid prototyping model and loaded onto the learning management system (LMS), which is http://www.enaged.co.kr. Fifty-four Korean agricultural high school students were randomly assigned into three groups (face-to-face lecture, animation based e-learning, and video based e-learning group). The students of the e-learning group logged on the LMS in school computer lab and completed each e-learning. All students were required to take a pretest and posttest before and after learning under the direction of the subject teacher. A one-way analysis of covariance was administered to verify whether there was any difference between face-to-face lecture and e-learning in terms of students' learning outcomes after controlling the covariate variable, pretest score. According to the results, no differences between animation based and video based e-learning as well as between face-to-face learning and e-learning were identified. Findings suggest that the use of well designed e-learning could be worthy even in agricultural education, which stresses hands-on experience and lab activities if e-learning was used appropriately in combination with conventional learning. Further research is also suggested, focusing on a preference of e-learning content type and its relationship with learning outcome.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1874-1884
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• 1996

In this paper, we proposed automatic searching algorithm of thresholding value using multilevel thresholding for face segmentation from input bust image effectively. The proposed algorithm extracted the thresholding value of brightness that is formed background region, face region and hair region without illumination, background and face size from input image. The statistical projection analysis project the brightness of multilevel thresholding image into horizontal and vertical direction and decide the thresholding value of face. And the algorithm extracted elliptical type block of face from input image in order to reduce the back ground region and hair region efficiently. The proposed algorithm can reduce searching area of feature extraction and processing time for face recognication.