• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12C
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• pp.984-994
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• 2010

Pose variation is a critical problem in face recognition. Three-dimensional(3D) face recognition techniques have been proposed, as 3D data contains depth information that may allow problems of pose variation to be handled more effectively than with 2D face recognition methods. This paper proposes a pose-normalized 3D face modeling method that translates and rotates any pose angle to a frontal pose using a plane fitting method by Singular Value Decomposition(SVD). First, we reconstruct 3D face data with stereo vision method. Second, nose peak point is estimated by depth information and then the angle of pose is estimated by a facial plane fitting algorithm using four facial features. Next, using the estimated pose angle, the 3D face is translated and rotated to a frontal pose. To demonstrate the effectiveness of the proposed method, we designed 2D and 3D face recognition experiments. The experimental results show that the performance of the normalized 3D face recognition method is superior to that of an un-normalized 3D face recognition method for overcoming the problems of pose variation.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.833-840
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• 2010

In this paper a method for detecting and extracting the face from the image in YCbCr spaceis proposed. The face region is obtained from the complex original image by using the difference method and the face color information is taken from the reduced face region throughthe Ellipse clustering method. The experimental results showed that the proposed method can efficiently detect and extract the face from the original image under the general light intensity except for low luminance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.11-20
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• 2010

In this paper we propose a face detection system which consists of a method of face candidate extraction using skin color and a method of face verification using the feature of facial structure. Firstly, the proposed extraction method of face candidate uses the image segmentation and merging algorithm in the regions of skin color and the neighboring regions of skin color. These two algorithms make it possible to select the face candidates from the variety of faces in the image with complicated backgrounds. Secondly, by using the partial face classifier, the proposed face validation method verifies the feature of face structure and then classifies face and non-face. This classifier uses face images only in the learning process and does not consider non-face images in order to use less number of training images. In the experimental, the proposed method of face candidate extraction can find more 9.55% faces on average as face candidates than other methods. Also in the experiment of face and non-face classification, the proposed face validation method obtains the face classification rate on the average 4.97% higher than other face/non-face classifiers when the non-face classification rate is about 99%.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.417-417
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• 2000

This paper proposes a face detection and recognition method that combines the template matching method and the eigenface method with the neural network. In the face extraction step, the skin color information is used. Therefore, the search region is reduced. The global property of the face is achieved by the eigenface method. Face recognition is performed by a neural network that can learn the face property.

• 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 Technologic Dentistry
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• v.42 no.4
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• pp.379-387
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• 2020

Purpose: This study aimed to explore methods to improve the quality recognition and satisfaction level of non-face-to-face lectures among students in the departments of dental technology and dental hygiene. Methods: This survey was conducted to assess the status and preference of non-face-to-face lectures and the level of awareness and satisfaction regarding the quality of these lectures among 179 students of dental technology and 295 students of dental hygiene. Statistical analyses were performed using frequency analysis, independent sample t-test, one-way ANOVA (post-hoc Duncan), Welch analysis (post-hoc Games-Howell), and hierarchical multiple regression analysis. Results: Factors that affected the ability to assess the quality of non-face-to-face lectures were the department, the method of non-face-to-face lectures, the most preferred method for conducting lectures, the level of awareness regarding the quality of face-to-face lecture, and satisfaction level. It has 71.5% explanatory power. Moreover, factors that influenced the satisfaction level of non-face-to-face lectures included the department, grade, the highest satisfied non-face-to-face teaching method, the most effective theoretical non-face-to-face teaching method, the most preferred teaching methods, and the ability to assess quality of face-to-face lectures. It has 46.8% explanatory power. Conclusion: Non-face-to-face classes should be designed and developed for web-based programs to improve the motivation and achievement level of the students and encourage interaction between the professors and students. Our findings suggest that educators should strive to achieve optimal educational effects by efficiently combining face-to-face and non-face-to-face lectures.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.777-786
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• 2008

Human face avatar is important information in nowadays, such as describing real people in virtual world. In this paper, we have presented a face avatar creation and warping algorithm by using face feature analysis method, in order to detect face feature, we utilized local mean method based on facial feature appearance and face geometric information. Then detect facial candidates by using it's character in $YC_bC_r$ color space. Meanwhile, we also defined the rules which are based on face geometric information to limit searching range. For analyzing face feature, we used face feature points to describe their feature, and analyzed geometry relationship of these feature points to create the face avatar. Then we have carried out simulation on PC and embed mobile device such as PDA and mobile phone to evaluate efficiency of the proposed algorithm. From the simulation results, we can confirm that our proposed algorithm will have an outstanding performance and it's execution speed can also be acceptable.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.776-777
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• 2017

The grain burn-back analysis has been required for the calculation of the solid propellant performance. The conventional grain burn-back analysis uses the level set method, but problems in the moving surface analysis may occur. In this study, the face offsetting method has been used for analyzing the moving surface. As results, the face offsetting method has been proven to be useful method for the grain burn-back analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2213-2216
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• 2003

In this paper, we propose the face identification method which is robust for lighting based on the feature points method. First of all, the proposed method extracts an edge of facial feature. Then, by the hough transform, it determines ellipse parameters of each facial feature from the extracted edge. Finally, proposed method performs the face identification by using parameters. Even if face image is taken under various lighting condition, it is easy to extract the facial feature edge. Moreover, it is possible to extract a subject even if the object has not appeared enough because this method extracts approximately the parameters by the hough transformation. Therefore, proposed method is robust for the lighting condition compared with conventional method. In order to show the effectiveness of the proposed method, computer simulations are done by using the real images.

• International Journal of Contents
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• v.9 no.3
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• pp.9-14
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• 2013

In this paper, we will examine single face tracking algorithms with scaling function in a mobile device. Face detection and tracking either in PC or mobile device with scaling function is an unsolved problem. Standard single face tracking method with particle filter has a problem in tracking the objects where the object can move closer or farther from the camera. Therefore, we create an algorithm which can work in a mobile device and perform a scaling function. The key idea of our proposed method is to extract the average of skin color in face detection, then we compare the skin color distribution between the detected face and the tracking face. This method works well if the face position is located in front of the camera. However, this method will not work if the camera moves closer from the initial point of detection. Apart from our weakness of algorithm, we can improve the accuracy of tracking.