• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.16-21
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• 2001

In this paper a virtual form of 3 dimensional face is synthesized from the two sheets of 2 dimensional photographs In this case two sheets of 2D face photographs, the front and the side photographs are used First of all a standard model for a general face is created and from this model the feature points which represents a construction of face are densely defined on part of ears. eyes, a nose and a lip but the other parts. for example, forehead, chin and hair are roughly determined because of flat region or the less individual points. Thereafter the side photograph is connected symmetrically on the left and right sides of the front image and it is gradually synthesized by use of affine transformation method. In order to remove the difference of color and brightness from the junction part, a linear interpolation method is used. As a result it is confirmed that the proposed model which general model of a face can be obtain the 3D virtual image of the individual face.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.328-333
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• 2003

This paper did to do with 18 muscle pairs that do fetters in anatomy that influence in facial expression change and mix motion of muscle for face facial animation. After set and change mash and make standard model in individual's image, did mapping to mash using individual facial front side and side image to raise truth stuff. Muscle model who become motive power that can do animation used facial expression creation correcting Waters' muscle model. Created deformed face that texture is dressed using these method. Also, 6 facial expression that Ekman proposes did animation.

• Korean Management Science Review
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• v.33 no.3
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• pp.65-87
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• 2016

Being the FinTech technologies rapidly developed, the non face-to-face private loan market is also growing dramatically. While the real-world interests in this market are keen, the empirical studies on the issue are few compared to its prospective impact on credit loan market. This paper suggests a credit scoring model for the non face-to-face private loan employing the ratings approach (the absolute measurement method) of AHP. Analyzing a sample of data consisting of 460,000 transaction records over an 8-year period in the United States, we develop a scoring model for the non face-to-face private loan screening, and validate the model for the practical usage. Conducting sensitivity analysis, we suggest customized cut-off points for the loan execution to suit each individual loan institution's need.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.6
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• pp.546-552
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• 2016

We present a quantitative evaluation of an algorithm for model-based face recognition. The algorithm actively learns how individual faces vary through video sequences, providing on-line suppression of confounding factors such as expression, lighting and pose. By actively decoupling sources of image variation, the algorithm provides a framework in which identity evidence can be integrated over a sequence. We demonstrate that face recognition can be considerably improved by the analysis of video sequences. The method presented is widely applicable in many multi-class interpretation problems.

• Annual Conference of KIPS
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• 2022.11a
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• pp.493-494
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• 2022

The development of livestock faces intensive farming results in a rising need for recognition of individual animals such as cows and pigs is related to high traceability. In this paper, we present a non-invasive biometrics systematic approach based on the deep-learning classification model to pig face identification. Firstly, in our systematic method, we build a ROS data collection system block to collect 10 pig face data images. Secondly, we proposed a preprocessing block in that we utilize the SSIM method to filter some images of collected images that have high similarity. Thirdly, we employ the improved image classification model of CNN (ViT), which uses the finetuning and pretraining technique to recognize the individual pig face. Finally, our proposed method achieves the accuracy about 98.66%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.322-327
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• 2003

Based on dynamic muscle model to construct efficient face animation in this paper 30 face modelling techniques propose. Composed face muscle by faceline that connect 256 point and this point based on dynamic muscle model, and constructed wireframe because using this. After compose standard model who use wireframe, because using front side and side 2D picture, enforce texture mapping and created 3D individual face model. Used front side of characteristic points and side part for correct mapping, after make face that have texture coordinates using 2D coordinate of front side image and front side characteristic points, constructed face that have texture coordinates using 2D coordinate of side image and side characteristic points.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.814-817
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• 2002

High-dimensional data with two or more attributes are considered. A typical example of such data is face images of various individuals and expressions. In these cases, collecting a complete data set is often difficult since the number of combinations can be large. In the present study, we propose a method to interpolate data of missing combinations from other data. If this becomes possible, robust recognition of multiple attributes is expectable. The key of this subject is appropriate extraction of the similarity that the face images of same individual or same expression have. Bilinear model ［1］has been proposed as a solution of this subjcet. However, experiments on application of bilinear model to classification of face images resulted in low performance ［2］. In order to overcome the limit of bilinear model, in this research, a nonlinear model on a neural network is adopted and usefulness of this model is experimentally confirmed.

• Quality Improvement in Health Care
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• v.30 no.1
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• pp.55-75
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• 2024

Purpose: This study aimed to elucidate the intention to use non-face-to-face treatment, which was temporarily allowed during the coronavirus disease 2019 pandemic. Based on the social cognitive theory, individual behavioral changes occur through the dynamic interaction of individual, environmental, and behavioral factors. Thus, we investigated the impact of personal, environmental, and behavioral factors on the acceptance of non-face-to-face treatment. Methods: A Web survey was conducted using Korea Research Panel between December 26 and 29, 2022, to examine the conceptual framework. The survey targeted adults aged 19 and older, regardless of whether they had used non-face-to-face treatment. A total of 502 responses were collected. Further, a three-step hierarchical regression analysis was conducted using SPSS Windows software version 25.0. Results: The study showed that 131 out of 502 respondents had experience using non-face-to-face treatment, while 371 did not. The factors that influenced the intention to accept non-face-to-face treatment included the general characteristics of the participants (women, underlying disease), personal factors (usefulness, cost savings, knowledge), and environmental factors (social norms, trust, perceived risk). The model demonstrated an explanatory power of 65%. Conclusion: The results of this study directly show that intention is linked to behavior through the interaction between personal and environmental factors. Further research is needed to explore additional factors influencing the intention to accept non-face-to-face treatment, enabling its effective use in preventing and treating various diseases, including infectious diseases.

• Archives of Plastic Surgery
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• v.37 no.4
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• pp.361-368
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• 2010

Purpose: Authors tried to analyze the influence of individual facial aesthetic subunits on the cognition of facial attractiveness in public and suggest a mathematical model which explain the facial attractiveness. Methods: Independent facial aesthetic subunits are extracted from facial photographs from three women (11 frontal and 7 lateral aesthetic subunits). Each facial subunits of three women are rated in terms of relative rank by 164 peoples (68 man and 96 woman, average age was 32.4, and ranged ${\pm}$ 9.8 years). $x^2$-test and categorical regression analysis were performed. Results: There was no difference in the aesthetic preference in terms of ages or sexes in large. Beautification of individual aesthetic subunits can predict the overall facial attractiveness up to 42.1% in frontal face (Adjusted $R^2$=0.421, F=6.39, p=0.000 < 0.05) and 22.7% in lateral face (Adjusted $R^2$=0.227, F=4.42, p=0.000 < 0.05). Aesthetic appearance of eyes (p=0.001), upper face (p=0.034) in frontal face and midface (p=0.000) in lateral face are statistically important factors in the cognition of facial attractiveness. Conclusion: Authors experimently proved that harmony and balance among facial aesthetic subunits are the most important factors, in embarking on facial aesthetic plastic surgery, for better enhancement of facial attractiveness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.887-898
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• 2005

In this paper, we introduce a probabilistic model based on Bayesian networks BNs) for recognizing human fatigue. First of all, we measured face feature information such as eyelid movement, gaze, head movement, and facial expression by IR illumination. But, an individual face feature information does not provide enough information to determine human fatigue. Therefore in this paper, a Bayesian network model was constructed to fuse as many as possible fatigue cause parameters and face feature information for probabilistic inferring human fatigue. The MSBNX simulation result ending a 0.95 BN fatigue index threshold. As a result of the experiment, when comparisons are inferred BN fatigue index and the TOVA response time, there is a mutual correlation and from this information we can conclude that this method is very effective at recognizing a human fatigue.