• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.134-138
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• Archives of Plastic Surgery
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• v.37 no.4
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• pp.351-360
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• 2010

Purpose: As the influence of mass media increases, the general standard of attractiveness or beauty of a face also changes. The primary purpose of the study is to find out the factors of the attractive and beautiful face recognized by public. Methods: We picked out standard model photography and operated with Adobe$^{(R)}$ Photoshop$^{(R)}$ and Monariza$^{(R)}$ virtual plastic surgery program. The contour of face, eye, nose, forehead, zygoma, chin and proportion of upper, middle, lower face were changed. The interview survey was conducted through structured standard photo for 310 respondents. That was utilized in the final analysis. Multiple regression analysis was executed by SPSS 12.0. It was used to deal with statistical data and all the other necessary analysis. Results: According to general characteristics of the respondents, many differences were found in preferred face and facial aesthetic subunits. The younger generation preferred the lozenge and inverted triangle shape contour. The respondents over 40 of age preferred the egg shape contour. In chin and zygoma contour, the respondents at the age of 20 preferred distinctly small chin and relatively small lower face. On the other hand, the respondents over 40 of age preferred the wide zygoma relatively. In the proportion of upper, middle, lower face, 51.0% of respondents answered 1 : 1 : 1. If they want to have an aesthetic operation, they preferred protruding forehead. Also they preferred the small chin and V-shaped chin in frontal view. Conclusion: Many respondents preferred to have a plastic surgery for the better facial subunit. The statistical evidence from this study suggests that the harmony and balance of facial aesthetic subunits make attractive and beautiful face.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.107-114
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• 2016

Recently, 3D related technology has become a hot topic for IT. 3D technologies such as 3DTV, Kinect and 3D printers are becoming more and more popular. According to the flow of the times, the goal of this study is that the general public is exposed to 3D technology easily. we have developed a web-based application program that enables 3D modeling of facial front and side photographs using a mobile phone. In order to realize 3D modeling, two photographs (front and side) are photographed with a mobile camera, and ASM (Active Shape Model) and skin binarization technique are used to extract facial height such as nose from facial and side photographs. Three-dimensional coordinates are generated using the face extracted from the front photograph and the face height obtained from the side photograph. Using the 3-D coordinates generated for the standard face model modeled with the standard face as a control point, the face becomes the face of the subject when the RBF (Radial Basis Function) interpolation method is used. Also, in order to cover the face with the modified face model, the control point found in the front photograph is mapped to the texture map coordinate to generate the texture image. Finally, the deformed face model is covered with a texture image, and the 3D modeled image is displayed to the user.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.10a
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• pp.193-200
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• 1999

Since Broms and Bennermark(1967) suggested the face stability criterion based on laboratory extrusion tests and field observations, the face stability of a tunnel driven in cohesive material has been studied by several authors. And recently, more general solution for the tunnel front is given by Leca and Panet(1988). They adopted a limit state design concept to evaluate the face stability of a shallow tunnel driven into cohesionless material and showed that the calculated upper bound solution represented the actual behavior reasonably well. In this study, two factors are simultaneously considered for assessing tunnel face stability: One is the effective stress acting on the tunnel front calculated by upper bound solution; and the other is the seepage force calculated by numerical analysis under the condition of steady state ground water flow. The model tests were performed to evaluate the seepage force acting on the tunnel front and these results were compared with results of numerical analysis. Consequently, the methodology to evaluate the stability of a tunnel face including limit analysis and seepage analysis is suggested under the condition of steady state ground water flow.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.647-650
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• 2002

The human faces do not have distinct features unlike other general objects. In general the features of eyes, nose and mouth which are first recognized when human being see the face are defined. These features have different characteristics depending on different human face. In this paper, We propose a face recognition algorithm using the hidden Markov model(HMM). In the preprocessing stage, we find edges of a face using the locally adaptive threshold scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability values calculated by the HMM to input data. Then the input face is recognized by the euclidean distance of face feature vector and the cross-correlation between the input image and the database image. Computer simulation shows that the proposed HMM algorithm gives higher recognition rate compared with conventional face recognition algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.38-43
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• 2008

Skin color information is an important feature for face region detection in color images. This can detect face region using statistical skin color model who is created from skin color information. However, due to the including of different race of people's skin color points, this general statistical model is not accurate enough to detect each specific image as we expected. This paper proposes method to detect correctly face region in various color image that other complexion part is included. In this method set face candidate region applying complexion Gausian distribution based on YCbCr skin color model and applied mathematical morphology to remove noise part and part except face region in color image. And achieved correct face region detection because using Haar-like feature. This approach is capable to distinguish face region from extremely similar skin colors, such as neck skin color or am skin color. Experimental results show that our method can effectively improve face detection results.

• Journal of the Korean Society for Railway
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• v.16 no.1
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• pp.47-51
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• 2013

In this study, we carried out an experimental shield TBM excavation model test using a down-scale device in soft clay, to understand tunnel-face stability properties in relation to changes in slurry pressure. We performed five tests according to tunnel depth (0.5D, 0.75D, 1.0D, 1.25D, 1.5D), and compared theoretical tunnel-face pressure with model test results. The range in theoretical tunnel-face slurry pressure ($P_{min}{\leq}P_{slurry\;pressure}{\leq}P_{max}$), which is determined by earth pressure and water level, was very similar to the model test result. This result was due to the more isotropic condition of the soft clay ground, than of rocky ground.

• Smart Media Journal
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• v.7 no.4
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• pp.61-69
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• 2018

Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2171-2185
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• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.