• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.201-210
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• 2023

Deep learning-based face verificattion model show high performance and are used in many fields, but there is a possibility the user's face image may be leaked in the process of inputting the face image to the model. Althoughde-identification technology exists as a method for minimizing the exposure of face features, there is a problemin that verification performance decreases when the existing technology is applied. In this paper, after combining the face features of other person, a de-identified face image is created through StyleGAN. In addition, we propose a method of optimizingthe combining ratio of features according to the face verification model using HopSkipJumpAttack. We visualize the images generated by the proposed method to check the de-identification performance, and evaluate the ability to maintain the performance of the face verification model through experiments. That is, face verification can be performed using the de-identified image generated through the proposed method, and leakage of face personal information can be prevented.

• 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 Geotechical Society Conference
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• 2005.03a
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• pp.1335-1342
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• 2005

In this paper, there is intended to introduce the new tunnel face shape, that is concave shaped face, and discusses its effects on the tunnel stabilization. Therefore, a comparative analysis in which the stability of a concave face was compared to that of a conventional plane face on the basis of displacement patterns in the tunnel face was conducted using a model test. In order to check and confirm displacement patterns on the concave face according to the radius of curvature as well as those around the face according to lateral pressure coefficient(k), two experimental concave models, produced at a scale of 1:2 and 1:5(tunnel radius), of the forefront of the curved area extended from plane face was built and tested.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.187-194
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• 2021

This paper presents a margin-based face liveness detection method with behavioral confirmation to prevent spoofing attacks using deep learning techniques. The proposed method provides a possibility to prevent biometric person authentication systems from replay and printed spoofing attacks. For this work, a set of real face images and fake face images was collected and a face liveness detection model is trained on the constructed dataset. Traditional face liveness detection methods exploit the face image covering only the face regions of the human head image. However, outside of this region of interest (ROI) might include useful features such as phone edges and fingers. The proposed face liveness detection method was experimentally tested on the author's own dataset. Collected databases are trained and experimental results show that the trained model distinguishes real face images and fake images correctly.

• Journal of Engineering Education Research
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• v.25 no.1
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• pp.46-54
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• 2022

This study attempted non-face-to-face collaborative product development practice that can respond to the spread of COVID-19 by expanding existing product data management system-based product development practice. For the complete non-face-to-face product development practice, it utilized prototype development using a 3D paper model, an online class management system and social media for classes and meetings. As a result of applying the non-face-to-face method, product developments of 26 practice teams have been completed without any failures. Therefore, through this study, the author can confirm that it is possible to provide the complete non-face-to-face collaborative product development practice based on product data management systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.35-43
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• 2010

1D PCA utilized in the face appearance-based face recognition methods such as eigenface-based face recognition method may lead to less face representative power and more computational cost due to the resulting 1D face appearance data vector of high dimensionality. To resolve such problems of 1D PCA, 2D PCA-based face recognition methods had been developed. However, the face representation model obtained by direct application of 2D PCA to a face image set includes both face common features and face distinctive identity features. Face common features not only prevent face recognizability but also cause more computational cost. In this paper, we first develope a model of a face distinctive identity feature subspace separated from the effects of face common features in the face feature space obtained by application of 2D PCA analysis. Then, a novel robust face recognition based on the face distinctive identity feature subspace model is proposed. The proposed face recognition method based on the face distinctive identity feature subspace shows better performance than the conventional PCA-based methods (1D PCA-based one and 2D PCA-based one) with respect to recognition rate and processing time since it depends only on the face distinctive identity features. This is verified through various experiments using Yale A and IMM face database consisting of face images with various face poses under various illumination conditions.

• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.45-53
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• 2022

The avionics industry has recently adopted an open architecture to increase software portability and reduce the development schedule and cost associated with changing hardware equipment. This paper presents a data modeling method compliant with FACE, a widely used open avionics architecture. A FACE data model is designed and implemented to output data from VOR/ILS avionics equipment. A FACE Conformance Test Suite (CTS) program is utilised to verify that the data model meets FACE standards. The proposed data modeling method is expected to improve the development schedule and cost associated with modifying communication methods and ICDs (Interface Control Documents).

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.96-99
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• 2020

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1582-1590
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• 2022

Due to the situation of the widespread of the coronavirus, which causes the problem of lack of face image data occluded by masks at recent time, in order to solve the related problems, this paper proposes a method to generate face images with masks using a combination of generative adversarial networks and spatial transformation networks based on CNN model. The system we proposed in this paper is based on the GAN, combined with multi-scale convolution kernels to extract features at different details of the human face images, and used Wasserstein divergence as the measure of the distance between real samples and synthetic samples in order to optimize Generator performance. Experiments show that the proposed method can effectively put masks on face images with high efficiency and fast reaction time and the synthesized human face images are pretty natural and real.