• 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%.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.125-133
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• 2021

Due to Covid-19, the un-contact culture has affected society as a whole, and the methods of education conducted offline has been greatly affected. In the private education of preparing for university entrance, the public official examinations and certification acquisition, the method of online education has been shown to have positive effects. While private class and school class which have offered in off-line to cope with rapid changes caused various problems such as decline in quality for education. Due to the characteristic of design class, practical training is important. As interactive feedback between students and educators is more important than one-way of delivering knowledge while class is conducted in online, educators have a challenge when they prepare for class. This study handles the methods of online education for the purpose of practical education methods in university nowadays, Especially, the non-face-to-face education methods for game animation production. Based on this study, I propose an effective educational method with non-face-to-face class that allows students to be satisfied and increases their knowledge, beyond face-to-face class.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.21-28
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• 2011

A 3D face shape derived from 2D images may be useful in many applications, such as face recognition, face synthesis and human computer interaction. To do this, we develop a fast 3D Active Appearance Model (3D-AAM) method using depth estimation. The training images include specific 3D face poses which are extremely different from one another. The landmark's depth information of landmarks is estimated from the training image sequence by using the approximated Jacobian matrix. It is added at the test phase to deal with the 3D pose variations of the input face. Our experimental results show that the proposed method can efficiently fit the face shape, including the variations of facial expressions and 3D pose variations, better than the typical AAM, and can estimate accurate 3D face shape from images.

• Journal of International Society for Simulation Surgery
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• v.3 no.2
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• pp.46-48
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• 2016

Purpose In face reocognition area, estimating occlusion in face images is on the rise. In this paper, we propose a new face recognition algorithm based on dual sparse representation to solve this problem. Method Each face image is partitioned into several pieces and sparse representation is implemented in each part. Then, some parts that have large sparse concentration index are combined and sparse representation is performed one more time. Each test sample is classified by using the final sparse coefficient where correlation between the test sample and training sample is applied. Results The recognition rate of the proposed algorithm is higher than that of the basic sparse representation classification. Conclusion The proposed method can be applied in real life which needs to identify someone exactly whether the person disguises his face or not.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.17-30
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• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• Journal of Science Education
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• v.45 no.2
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• pp.257-273
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• 2021

This study explores classes for pre-service mathematics teachers. The purpose of this study is to examine the differences between 'non-face-to-face' classes & 'face-to-face' classes, as well as the differences in learning outcomes between these two methods. A Professors' Learning Group was formed to effectively carry out this study. Through this learning group, we shared how to plan and operate the lecture. The subjects of this study are 'non-face-to-face calculus courses & face-to-face calculus courses' and 'non-face mathematics education theory courses & face-to-face mathematics education theory courses." Specifically, in these two pairs of courses, we analyze the differences in course management and the differences in the outcomes of students' assessments. Non-face-to-face classes were planned, developed, implemented and evaluated based on the 'non-face class design model.' The results of this study are as follows: First, we explored the differences between 'non-face-to-face classes/mixed classes' and 'face-to-face classes.' Second, the achievement results in calculus courses were higher in face-to-face classes than in non-face classes. Third, the results of achievements in mathematics education theory courses were higher in mixed classes than in face-to-face classes. Through the results of this study, we hope that the non-face-to-face class capabilities can be improved in pre-service mathematics teacher training.

• Physical Therapy Korea
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• v.30 no.1
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• pp.50-58
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• 2023

Background: This study was carried out to determine whether non-face-to-face physical therapy would have similar exercise effects to face-to-face physical therapy. Hence, we developed an approach for patients, unable to visit hospitals due to circumstances such as the COVID-19 pandemic, to conduct physical therapy comfortably at home. Objects: This study aimed to compare the effects of a face-to-face and a non-face-to-face physical therapy treatment on improving a rounded shoulder posture. Methods: The participants with rounded shoulders were randomly divided into a face-toface group (n = 15) and a non-face-to-face group (n = 15), and each group performed exercises for four weeks. The exercise program consisted of the bare hands exercise, Thera-Band exercise, and foam roller exercise. The participants in the face-to-face group came to a designated place to perform their exercises, and those in the non-face-to-face group performed the exercises at their own home using Google Meet (Google). Acromial height, total scapular distance (TSD), shoulder pain and dysfunction index (SPADI), and pectoralis minor thickness were measured. Data analysis was performed using the R Statistical Software (R Core Team), and a normality test was performed using the Shapiro-Wilk test. Results: There were no significant differences between the face-to-face and the non-face-toface groups (p > 0.05). When comparing the differences before and after the exercises, both the face-to-face and the non-face-to-face groups showed significant differences in acromial height, SPADI, and pectoralis minor thickness (p < 0.05), and both groups showed no significant difference in TSD before and after the exercises (p > 0.05). Conclusion: The results of this study support the results of previous studies reporting that shoulder stabilization exercise and pectoralis minor stretching training improves round shoulders. In addition, this study revealed that both the face-to-face and the non-face-to-face physical therapy treatments had therapeutic effects.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.81-96
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• 2024

This study analyzed the differences in the effects on entrepreneurship and entrepreneurial willingness of college students under the coronavirus pandemic by dividing theoretical education into practical education, face-to-face education, and non-face-to-face education, and analyzed the differences in the effects on entrepreneurship and entrepreneurship willingness according to the education method. This study conducted entrepreneurship education for 552 students at a comprehensive university in Chungcheong-do, Korea, and analyzed the sample by dividing it into theoretical and practical education, face-to-face education, and non-face-to-face education. In addition, a two-way repeated measures ANOVA was conducted to determine whether there were differences in the entrepreneurship education course operation form according to the pre- and post-education time points. The results showed that, first, the difference between the effectiveness of entrepreneurship education before and after theoretical and practical education was significant, and the entrepreneurship of practical education was higher than that of theoretical education after education. In the test of pre- and post-training differences in entrepreneurial intention, the difference in effectiveness was significant only in practical training. Second, the results of the repeated measures ANOVA analysis of the course operation type of theoretical and practical courses according to the difference between the pre- and post-education time points showed that there were differences in the entrepreneurship effectiveness of theoretical and practical courses according to the time point of education. Third, the difference in the effectiveness of entrepreneurship education according to face-to-face and non-face-to-face education was significant, and only the effect of non-face-to-face education on entrepreneurial intention was significant before and after education. Fourth, the results of repeated measures ANOVA analysis of face-to-face and non-face-to-face course operation type showed that the effect of face-to-face and non-face-to-face entrepreneurship education differed depending on the time of education. The pre-post difference in entrepreneurial intention was significant only for the non-face-to-face program. The implication of this study is that in order to increase the effectiveness of entrepreneurship and entrepreneurial will among university students, it is necessary to expand the amount of practical classes in which students actively participate in activities related to entrepreneurship. In addition, in order to increase the effectiveness of entrepreneurial will, a non-face-to-face education method that utilizes the metaverse space and increases the role of each student can contribute to increasing the effectiveness of entrepreneurial will.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.33-36
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• 2001

We introduce, in this paper, the face recognition method that improves recognition rate and training time in eigen system. To increase recognition rate we use Gabor filter. To reduce the increasing training time owing to use Gabor filtering, we extract new feature vectors that are made with average and standard deviation. In experimental results, we get higher recognition rate and shorter training time in improved system than it in original eigen system.

• Journal of KIISE
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• v.41 no.11
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• pp.921-926
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• 2014

Face detection refers to the process extracting facial regions in an input image, which can improve speed and accuracy of recognition or authorization system, and has diverse applicability. Since conventional works have tried to detect faces based on the whole shape of faces, its detection performance can be degraded by occlusion made with accessories or parts of body. In this paper we propose a method combining local feature descriptors and probability modeling in order to detect partially occluded face effectively. In training stage, we represent an image as a set of local feature descriptors and estimate a statistical model for normal faces. When the test image is given, we find a region that is most similar to face using our face model constructed in training stage. According to experimental results with benchmark data set, we confirmed the effect of proposed method on detecting partially occluded face.