• International journal of advanced smart convergence
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• v.8 no.4
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.558-572
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• 2019

Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

• Journal of muscle and joint health
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• v.29 no.1
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• pp.28-40
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• 2022

Purpose: The purpose of this study is to reduce loneliness through a systematic review by analyzing the effectiveness, composition and method of non-face-to-face services on the elderly living in the local community. Methods: From June 11 to 15, 2021, related papers were searched using six databases: Ovid-Medline, Ovid-Embase, Cochrane library, KISS, Koreamed, and RISS. Two authors independently assessed the quality of selected studies and data was synthesized. Results: Non-face-to-face services promoted loneliness and social isolation, social support and quality of life, other emotional responses, attitudes and usability, and diet and exercise. As the composition and method of services are being tried in various ways, it is necessary to develop a comprehensive service using ICT to provide systematic intervention to the elderly in the local community. Conclusion: Reflecting the difficulties in implementing face-to-face services due to COVID-19, it is expected to be used as basic data for developing comprehensive non-face-to-face services that meet the major needs of the elderly people and maintain the continuity of care.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.03a
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• pp.41-48
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• 2001

In this study, two factors are simultaneously considered for assessing tunnel face stability: one is the effective stress acting on the tunnel face calculated by upper bound solution; and the other is the seepage force calculated by numerical analysis under the condition of steady-state groundwater flow. The seepage forces calculated by numerical analysis are compared with the results of a model test. From the results of derivations of the upper bound solution with the consideration of seepage forces acting on the tunnel face, it could be found that the minimum support pressure for the face stability is equal to the sum of effective support pressure and seepage pressure acting on the tunnel face. Also it could be found that the average seepage pressure acting on the tunnel face is proportional to the hydrostatic pressure at the same elevation and the magnitude is about 22% of the hydrostatic pressure for the drainage type tunnel and about 28% for the water-proof type tunnel. The model tests performed with a tunnel model had a similar trend with the seepage pressure calculated by numerical analysis. From the model tests it could be also found that the collapse at the tunnel face occurs suddenly and leads to unlimited displacement.

• Journal of Technologic Dentistry
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• v.43 no.2
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• pp.62-68
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• 2021

Purpose: This study examined the correlations between the satisfaction and effectiveness of practical training and theory lectures under two conditions: face-to-face lectures and non-face-to-face online lectures. Methods: A survey of 436 public health student, whereafter SPSS 20.0 (IBM) was used on the data to conduct frequency, descriptive statistics, and exploratory factor analyses. The Cronbach's α value was estimated in a reliability analysis, and a simple regression analysis was conducted to verify the study hypothesis. Results: It was found that the students preferred pre-recorded lectures online for both practical training and theory, claiming that when compared with face-to-face lectures, these non-face-to-face lectures meant a shorter commute and the ability to repeat the content. However, it was admitted that technical issues such as facilities or access difficulties and lower concentration could be a problem. The hypothesis that course satisfaction affects lecture effectiveness was verified, with both the practical training and theory lectures found to have a statistically significant positive (+) effect. The explanatory power of student satisfaction on the effectiveness of the theory component was slightly higher than that of the practical training component, with the students having more positive perceptions on the necessity of face-to-face lectures in practical training than they did for those in theoretical instruction. Conclusion: Providing non-face-to-face online theory courses and face-to-face practical training courses could increase student satisfaction and lecture effectiveness.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.206-211
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• 2020

It is Face recognition is a very important process in image monitoring and it is a form of biometric technology. The recognition process involves many variables and is highly complex, so the software development has only begun recently with the development of hardware. Face detection technology using the CCTV is a process that precedes face analysis, and it is a technique that detects where the face is in the image. Research in face detection and recognition has been difficult because the human face reacts sensitively to different environmental conditions, such as lighting, color of skin, direction, angle and facial expression. The utility and importance of face recognition technology is coming into the limelight over time, but many aspects are being overlooked in the facial area detection technology that must precede face recognition. The system in this paper can detect tilted faces that cannot be detected by the AdaBoost detector and It could also be used to detect other objects.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.430-437
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• 2020

The proposed model implements a model that improves the face prediction rate and recognition rate through learning with an artificial neural network using face detection, landmark and face recognition algorithms. After landmarking in the face images of a specific person, the proposed model use the previously learned Caffe model to extract face detection and embedding vector 128D. The learning is learned by building machine learning algorithms such as support vector machine (SVM) and deep neural network (DNN). Face recognition is tested with a face image different from the learned figure using the learned model. As a result of the experiment, the result of learning with DNN rather than SVM showed better prediction rate and recognition rate. However, when the hidden layer of DNN is increased, the prediction rate increases but the recognition rate decreases. This is judged as overfitting caused by a small number of objects to be recognized. As a result of learning by adding a clear face image to the proposed model, it is confirmed that the result of high prediction rate and recognition rate can be obtained. This research will be able to obtain better recognition and prediction rates through effective deep learning establishment by utilizing more face image data.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1591-1600
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• 2007

Camera position information from 2D face image is very important for that make the virtual 3D face model synchronize to the real face at view point, and it is also very important for any other uses such as: human computer interface (face mouth), automatic camera control etc. We present an algorithm to detect human face region and mouth, based on special color features of face and mouth in $YC_bC_r$ color space. The algorithm constructs a mouth feature image based on $C_b\;and\;C_r$ values, and use pattern method to detect the mouth position. And then we use the geometrical relationship between mouth position information and face side boundary information to determine the camera position. Experimental results demonstrate the validity of the proposed algorithm and the Correct Determination Rate is accredited for applying it into practice.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.304-315
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• 2022

The effect on learning satisfaction was compared and analyzed according to the interaction of learners in non-face-to-face classes. 38 students enrolled in the Department of Chemistry Education at G University in Gyeongnam were selected for the study. As a result of analyzing the change in learning satisfaction according to learners' interactions, positive correlations between them were shown in non-face-to-face classes. The type of classes mainly consisted of non-face-to-face real-time classes, and despite the non-face-to-face classes environment, learners focused on classes and put a lot of effort to strengthen learning. Among learners' interactions, the effect of learner-content interaction on learning satisfaction was relatively the highest, while the effect of learner-learner interaction and learner-instructor interaction on learning satisfaction was low. It was found that learners' teaching-learning in non-face-to-face classes relied heavily on learning content, and interactions with fellow learners and instructors were very limited.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.115-124
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• 2009

The popular eigenfaces-based face recognition among proposed face recognition methods utilizes the eigenfaces obtained from applying PCA to a training face image set. Thus, it may not achieve a reliable performance under illumination environments different from that of training face images. In this paper, we propose an illumination-separate eigenfaces-based face recognition method, which excludes the effects of illumination as much as possible. The proposed method utilizes the illumination-separate eigenfaces which is obtained by orthogonal decomposition of the eigenface space of face model image set with respect to the constructed face illumination subspace. Through experiments, it is shown that the proposed face recognition method based on the illumination-separate eigenfaces performs more robustly under various illumination environments than the conventional eigenfaces-based face recognition method.