• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.1
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• pp.3-8
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• 2011

The work presented in this paper describes a Hidden Markov Model(HMM)-based framework for face recognition and face detection. The observation vectors used to characterize the statics of the HMM are obtained using the coefficients of the Karhuman-Loves Transform(KLT). The face recognition method presented in this paper reduces significantly the computational complexity of previous HMM-based face recognition systems, while slightly improving the recognition rate. In addition, the suggested method is more effective than the exiting ones in face extraction in terms of accuracy and others even under complex changes to the surroundings such as lighting.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.592-595
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• 2009

In this paper, we propose a face detection technique for still pictures which sequentially uses a skin-color model and a support vector machine (SVM). SVM is a learning algorithm for solving the classification problem. Some studies on face detection have reported superior results of SVM over neural networks. The SVM method searches for a face in a picture while changing the size of the window. The detection accuracy and the processing time of SVM vary largely depending on the complexity of the background of the picture or the size of the face. Therefore, we apply a face candidate area detection method using a skin-color model as a preprocessing technique. We compared the method using SVM alone with that of the proposed method in respect to face detection accuracy and processing time. As a result, the proposed method showed improved processing time while maintaining a high recognition rate.

• Journal of Sasang Constitutional Medicine
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• v.18 no.2
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• pp.25-33
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• 2006

1. Objectives Recently there have been studied the trials to take out the characteristics of Sasangin's face. 3 dimensional modeling is essential to find out sasangin's face. So the studies of standard face model and face type are necessary. 2. Methods I have reviewed the researches of standard facial modeling and facial type in the inside and outside of the country. 3. Results and Conclusions The Facial Definition Parameters are a very complex set of parameters defined by MPEG-4. It has defineds set of 84 feature points and 68 Facial Animation Parameters. Face type has been researched to divide into male and female, or the westerns and the orientals, or sasangin(Taeyangin, Taeumin, Soyangin, Soeumin).

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.476-486
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• 2007

This paper proposes a resolution enhancement method which can reconstruct high-resolution facial images from single-frame, low-resolution facial images. The proposed method is derived from example-based reconstruction methods and the morphable face model. In order to improve the performance of the example-based reconstruction, we propose the region-based reconstruction method which can maintain the characteristics of local facial regions. Also, in order to use the capability of the morphable face model to face resolution enhancement problems, we define the extended morphable face model in which an extended face is composed of a low-resolution face, its interpolated high-resolution face, and the high-resolution equivalent, and then an extended face is separated by an extended shape vector and an extended texture vector. The encouraging results show that the proposed methods can be used to improve the performance of face recognition systems, particularly to enhance the resolution of facial images captured from visual surveillance systems.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.79-91
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• 2004

Various facial pose detection and recognition has been a difficult problem. The problem is due to the fact that the distribution of various poses in a feature space is mere dispersed and more complicated than that of frontal faces, This thesis proposes a robust pose-expression-invariant face recognition method in order to overcome insufficiency of the existing face recognition system. First, we apply the TSL color model for detecting facial region and estimate the direction of face using facial features. The estimated pose vector is decomposed into X-V-Z axes, Second, the input face is mapped by deformable template using this vectors and 3D CANDIDE face model. Final. the mapped face is transformed to frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses, Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses and expressions.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.387-405
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• 2022

Purpose: This study aims to develop a communality model to measure the service quality that the customers encounter on face-to-face and non-face-to-face automobile insurance before and after insurance purchase. Methods: For these purposes, we conduct a five-step program to develop a comprehensive communality model to measure service quality except insurance purchase channels. Results: As a result, we find five communality factors: value-added service for automobile maintenance, value-added service for drivers, quickness, dispatch service, and convenience of the service process. And the relative importance of service quality factors was in the order of value-added service for drivers, the convenience of the service process, quickness, and dispatch service, respectively. Conclusion: This study developed the opportunities to enhance service quality to attract customers. And it contributes to the academic literature by reporting the industry-specific service quality model, which maintains a theoretical gap in the online and offline automobile insurance industry.

• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.737-744
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• 2009

This paper is to study and implement 3D Virtual Face Aesthetic Surgery System which provides more satisfaction by comparing the before-and-after plastic face surgery using 3D face model. For this study, we implemented 3D Face Model Generating System which resembles 2D image of the user based on 3D Korean standard face model and user's 2D pictures. The proposed 3D Virtual Face Aesthetic Surgery System in this paper consists of 3D Face Model Generating System, 3D Skin Texture Mapping System, and Detailed Adjustment System for reflecting the detailed description of face. The proposed system provides more satisfaction to the medical uses and stability in the surgery in compare with other existing systems.

• 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 Broadcast Engineering
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• v.24 no.3
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• pp.440-451
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• 2019

We propose a system that allows three dimensional manipulation of face in video. The 3D face manipulation of the proposed system overlays the 3D face model with the user 's manipulation on the face region of the video frame, and it allows 3D manipulation of the video in real time unlike existing applications or methods. To achieve this feature, first, the 3D morphable face model is registered with the image. At the same time, user's manipulation is applied to the registered model. Finally, the frame image mapped to the model as texture, and the texture-mapped and deformed model is rendered. Since this process requires lots of operations, parallel processing is adopted for real-time processing; the system is divided into modules according to functionalities, and each module runs in parallel on each thread. Experimental results show that specific parts of the face in video can be manipulated in real time.

• 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.