• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.359-362
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• 2016

Face is an information unique to each person such as Iris, fingerprints, etc,. Research on face recognition are in progress continuously from the past to the present. Through these research, various face recognition methods have appeared. Among these methods, there are face recognition algorithms using the face data composed in stereo. In this paper, Convolutional Neural Network with Stereo Images as input was used for face recognition. This method showed better performance than the result of stereo face recognition using PCA that is used frequently in face recognition.

• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.17-25
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• 2009

This paper suggests fuzzy-based Segment-Boost method and an effective method for face recognition using the fuzzy-based Segment-Boost. Fuzzy-based Segment-Boost eliminates the limitations of Segment-Boost, and it guarantees improved learning performance and the stability of the performance. By using the fuzzy theory, fuzzy-based Segment-Boost optimizes the selection number of sub-vectors, and leads the optimized learning performance. The fuzzy controller designed in this paper measures learning performance of the fuzzy-based Segment-Boost, and it controls the selection number of sub-vectors by inferring the optimized selection number. The simulation results show that the fuzzy controller inferred the selection number which is very approximate to the true optimized value. As a result, fuzzy-based Segment-Boost showed higher face recognition rate than compared boosting methods and it preserves the velocity of feature selection as fast as that of Segment-Boost. From the experimental results, it was proved that fuzzy-based Segment-Boost has improved and stable performances of learning, feature selection and face recognition.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.437-447
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• 2010

The purpose of this thesis was to design and develop an effective e-PBL(Problem-Based Learning) for cultivating consciousness of information and communication ethics. The proposed e-PBL is based on PBL which is one of the constructivism teaching-learning theories. Online learning and face-to-face classes were systematically combined for achieving the teaching-learning goals. And the main module for online learning run on Moodle, an open source learning management system. To examine educational effectiveness of the proposed e-PBL, an experimental study was conducted through the education content and method to the subject of two class in the second-grade of university located in OO city. For experiment 60 students(treatment group=30, control group=30) are participated. And they were randomly assigned to one of ten subgroups, comprising of six students, respectively. The results of this study showed that the education using proposed e-PBL is more effective in cultivating consciousness of information and communication ethics and learners responded positively than the education using traditional face-to-face PBL learning method.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.653-655
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• 2022

Face aging aims to synthesize future face images by reflecting the age factor on given faces. In recent years, deep learning-based approaches have made outstanding progress in simulating the aging process of the human face. However, generating accurate and high-quality aging faces is still intrinsically difficult. We propose a new method that incorporates gender information into the model, which achieves comparable and stable performance. Experimental results demonstrate that our method can preserve the identity well and generate diverse aged faces.

• 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 Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.1-9
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• 2020

As pet dogs rapidly increase in number, abandoned and lost dogs are also increasing in number. In Korea, animal registration has been in force since 2014, but the registration rate is not high owing to safety and effectiveness issues. Biometrics is attracting attention as an alternative. In order to increase the recognition rate from biometrics, it is necessary to collect biometric images in the same form as much as possible-from the face. This paper proposes a method to determine whether a dog is facing front or not in a real-time video. The proposed method detects the dog's eyes and nose using deep learning, and extracts five types of directional face information through the relative size and position of the detected face. Then, a machine learning classifier determines whether the dog is facing front or not. We used 2,000 dog images for learning, verification, and testing. YOLOv3 and YOLOv4 were used to detect the eyes and nose, and Multi-layer Perceptron (MLP), Random Forest (RF), and the Support Vector Machine (SVM) were used as classifiers. When YOLOv4 and the RF classifier were used with all five types of the proposed face orientation information, the face recognition rate was best, at 95.25%, and we found that real-time processing is possible.

• Journal of Engineering Education Research
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• v.24 no.5
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• pp.3-14
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• 2021

This study deals with the case of online-based project learning, which was designed for the purpose of university educational innovation and enhancing learners' competencies required by society, operated during the COVID-19 pandemic. The course was applied Python programming language, team-based project learning, and intensive course system, which is required by our society and companies in the era of the 4th industrial revolution. Also it was operated as a non-face-to-face online class, which would have been operated in an offline class if it had not been for Covid 19 pandemic, to explore the possibilities and educational effects of online learning. To do this, 32 university students participated in online-based project learning during 8 weeks, and then conducted a survey. The survey results were analyzed in terms of i) non-face-to-face online learning, ii) team-based project learning, and iii) application of the intensive course system. Results say that most of the learners were satisfied with the online learning, team-based project learning, and the intensive semester system applied in this course at a high level, and also they clearly presented the reasons. Thereby, it has been confirmed that the learners were already well aware of the pros and cons of each learning method. Based on these results, the implications were discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.352-353
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• 2018

Many deep learning approaches are studied for face detection in these days. However, there is still a performance problem to run efficiently on devices with limited resources. Our method can enhance the detection speed by decreasing the number of scaling for detection methods that use many different scaling per image to detect the different size of faces. Also, we keep our deep learning model easy to implement and small as possible. Moreover, it can be used for other special object detection problems but not only for face detection.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.49-56
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• 2011

Recently, animation contents has become extensively available along with the development of cultural industry. In this paper, we propose a method to analyze a face of animation character and extract 3D rotational information of the face. The suggested method first generates a dominant color model of a face by learning the face image of animation character. Our system then detects the face and its components with the model, and establishes two coordinate systems: base coordinate system and target coordinate system. Our system estimates three dimensional rotational information of the animation character face using the geometric relationship of the two coordinate systems. Finally, in order to visually represent the extracted 3D information, a 3D face model in which the rotation information is reflected is displayed. In experiments, we show that our method can extract 3D rotation information of a character face reasonably.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1213-1220
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• 2022

This study was conducted to understand how to derive educational methods to efficiently improve class satisfaction for 130 college students who experienced non-face-to-face classes during the last Covid-19. The appropriateness of class time, improvement of learning effect, continuity of non-face-to-face classes, and use of educational media were set as items corresponding to class satisfaction. Research was attempted to derive an educational method for efficient class satisfaction improvement by grasping the difference and relationship between variables.