• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.109-116
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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 for the identification of a face shape. Also it satisfies both efficiency of calculation and the function of detection. The algorithm proposed segmented the face area through pre-processing using a face shape as input information in an environment with a single camera and then identified the shape using a Self Organized Feature Map(SOFM). However, as it is not easy to exactly recognize a face area which is sensitive to light, it has a large degree of freedom, and there is a large error bound, to enhance the identification rate, rotation information on the face shape was made into a database and then a principal component analysis was conducted. Also, as there were fewer calculations due to the fewer dimensions, the time for real-time identification could be decreased.

• Smart Media Journal
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• v.11 no.8
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• pp.65-72
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• 2022

Due to COVID-19, the importance of non-face-to-face counseling is increasing as the face-to-face counseling method has progressed to non-face-to-face counseling. The advantage of non-face-to-face counseling is that it can be consulted online anytime, anywhere and is safe from COVID-19. However, it is difficult to understand the client's mind because it is difficult to communicate with non-verbal expressions. Therefore, it is important to recognize emotions by accurately analyzing text and voice in order to understand the client's mind well during non-face-to-face counseling. Therefore, in this paper, text data is vectorized using FastText after separating consonants, and voice data is vectorized by extracting features using Log Mel Spectrogram and MFCC respectively. We propose a multi-emotion recognition model that recognizes five emotions using vectorized data using an LSTM model. Multi-emotion recognition is calculated using RMSE. As a result of the experiment, the RMSE of the proposed model was 0.2174, which was the lowest error compared to the model using text and voice data, respectively.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.176-188
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• 2022

In this paper, we propose a compact method for anthropomorphism that uses Deep Convolutional Neural Networks (DCNN) to detect the similarities between a human face and an animal face. We also apply texture feature-based morphing between them. We propose a basic texture feature-based morphing system for morphing between human faces only. The entire anthropomorphism process starts with the creation of an animal face classifier using a parallel DCNN that determines the most similar animal face to a given human face. The significance of our network is that it contains four sets of convolutional functions that run in parallel, allowing it to extract more features than a linear DCNN network. Our employed texture feature algorithm-based automatic morphing system recognizes the facial features of the human face and takes the Control Points automatically, rather than the traditional human aiding manual morphing system, once the similarity was established. The simulation results show that our suggested DCNN surpasses its competitors with a 92.0% accuracy rate. It also ensures that the most similar animal classes are found, and the texture-based morphing technology automatically completes the morphing process, ensuring a smooth transition from one image to another.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.49-52
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• 2002

This paper presents a fast algorithm for face detection from color images on internet. We use Mahalanobis distance between standard skin color and actual pixel color on IQ color space to segment skin color regions. The skin color regions are the candidate face region. Further, the locations of eyes and mouth regions are found by computing average pixel values on horizontal and vertical pixel lines. The geometry of mouth and eye locations is compared to the standard face geometry to eliminate false face regions. Our Method is simple and fast so that it can be applied to face search engine for internet.

• Journal of Korean Tunnelling and Underground Space Association
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• v.18 no.5
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• pp.487-497
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• 2016

If a tunnel is excavated, the released stress is redistributed in the ground around the tunnel face, which lead the stress state of the surrounding ground of the tunnel and the load acting on the tunnel support to change. If the tunnel face deforms, the ground ahead of it is relaxed, and the earth pressure acting on it decreases. And if the displacement increases so much that, the ground ahead of the tunnel face reaches in failure state. At this time, load would be transferred longitudinally in the tunnel, depending on the cover and the face deformations. The longitudinal load transfers in the tunnels induced by the tunnelling has been often studied; however, the relation between the deformation of the tunnel face and the longitudinal load transfer was rarely studied. Therefore in this study assesses the characteristics of the longitudinal load transfer as the face was failed by displacement by conducting a model test in a shallow tunnel. In other words, the longitudinal load transfer of the tunnel with the progress of the face deform was measured by conducting a model test, beginning at the state of earth pressure at rest. As results of this study, most of the longitudinal load transfers occurred drastically at the beginning of the displacement of the tunnel face, and as the displacement of the face approached the ultimate displacement, it converged to the ultimate displacement at a gentler slope. In other words, when the ground ahead of the tunnel face was still in an elastic state, the longitudinally transferred load increased sharply at the beginning stage but it tended to increase gradually if it approached to the ultimate limit. Thus, it was noted that the earth pressure in the face and the longitudinal load transfer of the tunnel had the same decreasing tendency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2667-2682
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• 2015

Due to the wide application of face recognition (FR) in information security, surveillance, access control and others, it has received significantly increased attention from both the academic and industrial communities during the past several decades. However, partial face occlusion is one of the most challenging problems in face recognition issue. In this paper, a novel method based on linear regression-based classification (LRC) algorithm is proposed to address this problem. After all images are downsampled and divided into several blocks, we exploit the evaluator of each block to determine the clear blocks of the test face image by using linear regression technique. Then, the remained uncontaminated blocks are utilized to partial occluded face recognition issue. Furthermore, an improved Distance-based Evidence Fusion approach is proposed to decide in favor of the class with average value of corresponding minimum distance. Since this occlusion removing process uses a simple linear regression approach, the completely computational cost approximately equals to LRC and much lower than sparse representation-based classification (SRC) and extended-SRC (eSRC). Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and the performance is satisfactory. Through the comparison with the conventional methods (eigenface+NN, fisherfaces+NN) and the state-of-the-art methods (LRC, SRC and eSRC), the proposed method shows better performance and robustness.

• Knowledge Management Research
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• v.21 no.2
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• pp.119-135
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• 2020

Owing to pandemic (COVID-19), the traditional face-to-face education method has been changed to the non-face-to-face real-time online education methods. Using a real time-based video conference system, synchronous education can be adopted by face-to-face class easily. Specially, it is very important to minimize the difference in learning effects between face-to-face and non-face-to-face in Entrepreneurship education. In this study, in order to derive the factors that affect the satisfaction of learners in synchronous online education, authors collected data from learners taking a synchronous entrepreneurship course. Through previous research, learned the reality of education and the composition of lessons. Spatiotemporal effectiveness, mentor ability, and educational environment influence learning satisfaction. PLS-SEM results revealed that it was confirmed that only spatiotemporal effects affect learner satisfaction. However, the education environment (fluent operation and convenience of function use of real-time based online conference system) effect teaching presence, class structure, and spatiotemporal effects. Through this research, we hope to provide theoretical and practical support for developing effective teacher activities, proper lesson structure, convenient function of the conference system, and learner-centered online learning environment when developing synchronous online classes.

• Journal of muscle and joint health
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• v.25 no.3
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• pp.218-229
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• 2018

Purpose: The purpose of this study is to investigate relationships among workplace face to face bullying, cyber bullying, self-esteem, and turnover intention of hospital nurses, and to identify affecting factors for turnover intention through their relationships. Methods: Data were collected from 178 hospital nurses by self-reported questionnaire. The relationship among variables were analyzed with Pearson's coefficient correlation and affecting factors for turnover intention were identified by using multiple linear regression. Results: The mean score of turnover intention was $3.55{\pm}0.94$. Turnover intention was significantly different by age, marriage status, educational background, total experience as a nurse, designation, health status, bullying experience, and bullied experience. Turnover intention had positive relationships with workplace face to face bullying and hospital size, but negative relationships with self-esteem and health status. Workplace face to face bullying, health status and hospital size were identified as influencing factors in turnover intention. Conclusion: It is necessary to nursing community's efforts to decrease face to face bullying in order to lower the turnover intention of nurses. In this regard workplace bullying among nurses should be addressed using a comprehensive strategy that considers both individual and organizational factors. It is also necessary to nurse 's efforts to increase self-esteem.

• 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.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.10-12
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• 2015

Purpose It has been stated that patient satisfaction is the crucial factor for determining success in plastic surgery. The convergence of medical science and computer vision has made easier to satisfy patients who wants to have plastic surgery. In this paper, we try to apply 3D face modeling in plastic surgical area. Materials and Methods The author introduces a method for accurate 3D face modeling techniques using a statistical model-based 3D face modeling approach in a mirror system. Results We could successfully obtain highly accurate 3D face shape results. Conclusion The method suggested could be used for acquiring 3D face models from 2D face image and the result obtained from this could be effectively used for plastic surgical areas.