• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.1-7
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• 2006

In this paper, adaptive Facial Emotional Recognition system based on Gabor Wavelet Neural Network, considering six feature Points in face image to extract specific features of facial expression, is proposed. Levenberg-Marquardt-based training methodology is used to formulate initial network, including feature extraction stage. Therefore, heuristics in determining feature extraction process can be excluded. Moreover, to make an adaptive network for new user, Q-learning which has enhanced reward function and unsupervised fuzzy neural network model are used. Q-learning enables the system to ge optimal Gabor filters' sets which are capable of obtaining separable features, and Fuzzy Neural Network enables it to adapt to the user's change. Therefore, proposed system has a good on-line adaptation capability, meaning that it can trace the change of user's face continuously.

• Korean Journal of Cognitive Science
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• v.13 no.3
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• pp.11-21
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• 2002

Parke and Waters' model［1］ of muscle-based face deformation was used to develop a system that can synthesize facial expressions when the pleasure-displeasure and arousal-sleep coordinate values of internal states are specified. Facial expressions sampled from a database developed by Chung, Oh, Lee and Byun ［2］ and its underlying model of internal states were used to find rules for face deformation. The internal - state model included dimensional and categorical values of the sampled facial expressions. To find out deformation rules for each of the expressions, changes in the lengths of 21 facial muscles were measured. Then, a set of multiple regression analyses was performed to find out the relationship between the muscle lengths and internal states. The deformation rules obtained from the process turned out to produce natural-looking expressions when the internal states were specified by the pleasure-displeasure and arousal-sleep coordinate values. Such a result implies that the rules derived from a large scale database and regression analyses capturing the variations of individual muscles can be served as a useful and powerful tool for synthesizing facial expressions.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.1
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• pp.11-17
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• 2005

This study was performed to analysis the operation process when fabric pattern design was done by the use of CAD system compared with the manual work in order to determine the operation efficiency with the application of fabric design CAD system. The results of the study were as follows: 1. since 160,000 colors were supported by CAD system, color proposed by consumer was able to match exactly according to the its design. However, exact color matching was not possible by manual work. 2. Woven state of back of pattern design could be identified simultaneously with face of it for CAD system, while face and back of the fabrics should be designed separatedly in case of manual work. 3. Since the combination of warp and filling yarn was compatible with the fabric density in one repeat unit in CAD system, exact size of pattern design to be woven was able to expressed. 4. Only simple graphical expression by manual work was seen, while with the CAD system, texture and shade effect as well as graphical expression could be expressed and so fault could be reduced in advance with the simulation of actual feeling of fabrics in the screen. In conclusion, when CAD system will be introduced to the textile industry, operation time of designing pattern can be reduced. Since the operation is easy and simple, a beginner can operate CAD system easily. Thus, production and wage costs can be saved and this can be related directly with the improvement of productivity which is the main purpose of introducing CAD system.

• Journal of the HCI Society of Korea
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• v.9 no.1
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• pp.23-28
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• 2014

In this study we focus on the effect of the interaction between humans and reactive television when emotion recognition through facial expression mechanism is used. Most of today's user interfaces in electronic products are passive and are not properly fitted into users' needs. In terms of the user centered device, we propose that the emotion based reactive television is the most effective in interaction compared to other passive input products. We have developed and researched next generation cognitive TV models in user centered. In this paper we present a result of the experiment that had been taken with Fraunhofer IIS $SHORE^{TM}$ demo software version to measure emotion recognition. This new approach was based on the real time cognitive TV models and through this approach we studied the relationship between humans and cognitive TV. This study follows following steps: 1) Cognitive TV systems can be on automatic ON/OFF mode responding to motions of people 2) Cognitive TV can directly select channels as face changes (ex, Neutral Mode and Happy Mode, Sad Mode, Angry Mode) 3) Cognitive TV can detect emotion recognition from facial expression of people within the fixed time and then if Happy mode is detected the programs of TV would be shifted into funny or interesting shows and if Angry mode is detected it would be changed to moving or touching shows. In addition, we focus on improving the emotion recognition through facial expression. Furthermore, the improvement of cognition TV based on personal characteristics is needed for the different personality of users in human to computer interaction. In this manner, the study on how people feel and how cognitive TV responds accordingly, plus the effects of media as cognitive mechanism will be thoroughly discussed.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1187-1193
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• 2013

In this paper, we present realistic 3D head modeling and facial expression systems. For 3D head modeling, we perform generic model fitting to make individual head shape and texture mapping. To calculate the deformation function in the generic model fitting, we determine correspondence between individual heads and the generic model. Then, we reconstruct the feature points to 3D with simultaneously captured images from calibrated stereo camera. For texture mapping, we project the fitted generic model to image and map the texture in the predefined triangle mesh to generic model. To prevent extracting the wrong texture, we propose a simple method using a modified interpolation function. For generating 3D facial expression, we use the vector muscle based algorithm. For more realistic facial expression, we add the deformation of the skin according to the jaw rotation to basic vector muscle model and apply mass spring model. Finally, several 3D facial expression results are shown at the end of the paper.

• Korean Institute of Interior Design Journal
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• v.16 no.2 s.61
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• pp.79-86
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• 2007

The recent works of architecture and interior design take on too various aspects to be classified as a trend or tendency. Many subjects, seeking the superiority between form and function in space which was the issue of Modernism, got to face the works which transcend the limitation of architecture due to the development of a variety of media and technology these days. In result, we could see the works which are to be Included into the category of art rather than architecture. This study starts from the thought that the common aspects of these works are based on the strangeness to pursue uniqueness, peculiarities or novelty. 'The subject of this study, $'D\acute{e}paysement'$, creates a new relation, disconnected from customs, to an architecture which became familiar through defragment, fixation and abstraction by the habitual relation and makes it reborn as an architecture which gives us a fantasy and a pleasure, by converting into the shape and image which are different from its original shape when a stimulus is given to an architecture. This study starts from the artistic aspect which has a close correlation with the architecture. The subject, $'D\acute{e}paysement'$ could be historically considered in two aspects - $D\acute{e}paysement$ of Surrealism which developed in paintings and Defamiliarization of Russian Formalism in the area of literature. This study is carried in a category of $D\acute{e}paysement$, focusing on the visual aspect of the architecture. This study is composed as follows: In chapter 2, a theoretical research is performed according to the definition and meaning of the $D\acute{e}paysement$ expression techniques, its origination and evolution, and type classification by the features of expression techniques in the era of Surrealism when $D\acute{e}paysement$ developed most vigorously in the area of art. In chapter 3, the system which formalizes the expression method of the techniques is presented, focusing on the case analysis of the artistic works according to the types of $D\acute{e}paysement$. In chapter 4, the application of the expression techniques of $D\acute{e}paysement$ is discussed by conducting cases analysis of the modern interior design.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.162-172
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• 2017

In this paper, we propose facial expression recognition using CNN (Convolutional Neural Network), one of the deep learning technologies. To overcome the disadvantages of existing facial expression databases, various databases are used. In the proposed technique, we construct six facial expression data sets such as 'expressionless', 'happiness', 'sadness', 'angry', 'surprise', and 'disgust'. Pre-processing and data augmentation techniques are also applied to improve efficient learning and classification performance. In the existing CNN structure, the optimal CNN structure that best expresses the features of six facial expressions is found by adjusting the number of feature maps of the convolutional layer and the number of fully-connected layer nodes. Experimental results show that the proposed scheme achieves the highest classification performance of 96.88% while it takes the least time to pass through the CNN structure compared to other models.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.570-579
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• 2005

Facial expression recognition technology that has potentialities for applying various fields is appling on the man-machine interface development, human identification test, and restoration of facial expression by virtual model etc. Using sequential facial images, this study proposes a simpler method for detecting human facial expressions such as happiness, anger, surprise, and sadness. Moreover the proposed method can detect the facial expressions in the conditions of the sequential facial images which is not rigid motion. We identify the determinant face and elements of facial expressions and then estimates the feature regions of the elements by using information about color, size, and position. In the next step, the direction patterns of feature regions of each element are determined by using optical flows estimated gradient methods. Using the direction model proposed by this study, we match each direction patterns. The method identifies a facial expression based on the least minimum score of combination values between direction model and pattern matching for presenting each facial expression. In the experiments, this study verifies the validity of the Proposed methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.19-29
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• 2011

Until now, many face recognition methods have been proposed, most of them use a 1-dimensional feature vector which is vectorized the input image without feature extraction process or input image itself is used as a feature matrix. It is known that the face recognition methods using raw image yield deteriorated performance in databases whose have severe illumination changes. In this paper, we propose a face recognition method using local statistics of gradients and correlations which are good for illumination changes. BDIP (block difference of inverse probabilities) is chosen as a local statistics of gradients and two types of BVLC (block variation of local correlation coefficients) is chosen as local statistics of correlations. When a input image enters the system, it extracts the BDIP, BVLC1 and BVLC2 feature images, fuses them, obtaining feature matrix by $(2D)^2$ PCA transformation, and classifies it with training feature matrix by nearest classifier. From experiment results of four face databases, FERET, Weizmann, Yale B, Yale, we can see that the proposed method is more reliable than other six methods in lighting and facial expression.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.67-75
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• 2006

Facial expressions exhibit not only facial feature motions, but also subtle changes in illumination and appearance. Since it is difficult to generate realistic facial expressions by using only geometric deformations, detailed features such as textures should also be deformed to achieve more realistic expression. The existing methods such as the expression ratio image have drawbacks, in that detailed changes of complexion by lighting can not be generated properly. In this paper, we propose a nonlinear model for skin color change and a model-based synthesis method for facial expression that can apply realistic expression details under different lighting conditions. The proposed method is composed of the following three steps; automatic extraction of facial features using active appearance model and geometric deformation of expression using warping, generation of facial expression using a model for nonlinear skin color change, and synthesis of original face with generated expression using a blending ratio that is computed by the Euclidean distance transform. Experimental results show that the proposed method generate realistic facial expressions under various lighting conditions.