• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.395-402
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• 2009

This paper proposes an approach of a novel facial expression recognition to deal with different intensities to improve a performance of a facial expression recognition. Various expressions and intensities of each person make an affect to decrease the performance of the facial expression recognition. The effect of different intensities of facial expression has been seldom focused on. In this paper, a face expression template and an expression-intensity distribution model are introduced to recognize different facial expression intensities. These techniques, facial expression template and expression-intensity distribution model contribute to improve the performance of facial expression recognition by describing how the shift between multiple interest points in the vicinity of facial parts and facial parts varies for different facial expressions and its intensities. The proposed method has the distinct advantage that facial expression recognition with different intensities can be very easily performed with a simple calibration on video sequences as well as still images. Experimental results show a robustness that the method can recognize facial expression with weak intensities.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.18-26
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• 2008

This paper presents a phased visualization method of facial expression space that enables the user to control facial expression of 3D avatars by select a sequence of facial frames from the facial expression space. Our system based on this method creates the 2D facial expression space from approximately 2400 facial expression frames, which is the set of neutral expression and 11 motions. The facial expression control of 3D avatars is carried out in realtime when users navigate through facial expression space. But because facial expression space can phased expression control from radical expressions to detail expressions. So this system need phased visualization method. To phased visualization the facial expression space, this paper use fuzzy clustering. In the beginning, the system creates 11 clusters from the space of 2400 facial expressions. Every time the level of phase increases, the system doubles the number of clusters. At this time, the positions of cluster center and expression of the expression space were not equal. So, we fix the shortest expression from cluster center for cluster center. We let users use the system to control phased facial expression of 3D avatar, and evaluate the system based on the results.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.748-757
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• 2017

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.

• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.85-90
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• 2007

This paper presents a facial animation and expression control method that enables the animator to select any facial frames from the facial expression space, whose expression transfer paths the system can setup automatically. Our system creates the facial expression space from approximately 2500 captured facial frames. To create the facial expression space, we get distance between pairs of feature points on the face and visualize the space of expressions in 2D space by using the Multidimensional scaling(MDS). To setup most suitable expression transfer paths, we classify the facial expression space into four field on the basis of any facial expression state. And the system determine the state of expression in the shortest distance from every field, then the system transfer from the state of any expression to the nearest state of expression among thats. To complete setup, our system continue transfer by find second, third, or fourth near state of expression until finish. If the animator selects any key frames from facial expression space, our system setup expression transfer paths automatically. We let animators use the system to create example animations or to control facial expression, and evaluate the system based on the results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.2
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• pp.120-133
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• 2008

This paper presents a novel approach for facial motion tracking and facial expression cloning to create a realistic facial animation of a 3D avatar. The exact head pose estimation and facial expression tracking are critical issues that must be solved when developing vision-based computer animation. In this paper, we deal with these two problems. The proposed approach consists of two phases: dynamic head pose estimation and facial expression cloning. The dynamic head pose estimation can robustly estimate a 3D head pose from input video images. Given an initial reference template of a face image and the corresponding 3D head pose, the full head motion is recovered by projecting a cylindrical head model onto the face image. It is possible to recover the head pose regardless of light variations and self-occlusion by updating the template dynamically. In the phase of synthesizing the facial expression, the variations of the major facial feature points of the face images are tracked by using optical flow and the variations are retargeted to the 3D face model. At the same time, we exploit the RBF (Radial Basis Function) to deform the local area of the face model around the major feature points. Consequently, facial expression synthesis is done by directly tracking the variations of the major feature points and indirectly estimating the variations of the regional feature points. From the experiments, we can prove that the proposed vision-based facial expression cloning method automatically estimates the 3D head pose and produces realistic 3D facial expressions in real time.

• Journal of Information Science Theory and Practice
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• v.7 no.2
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• pp.32-39
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• 2019

A convolutional neural network (CNN) has been widely used in facial expression recognition (FER) because it can automatically learn discriminative appearance features from an expression image. To make full use of its discriminating capability, this paper suggests a simple but effective method for CNN based FER. Specifically, instead of an original expression image that contains facial appearance only, the expression image with facial geometry visualization is used as input to CNN. In this way, geometric and appearance features could be simultaneously learned, making CNN more discriminative for FER. A simple CNN extension is also presented in this paper, aiming to utilize geometric expression change derived from an expression image sequence. Experimental results on two public datasets (CK+ and MMI) show that CNN using facial geometry visualization clearly outperforms the conventional CNN using facial appearance only.

• 한국HCI학회:학술대회논문집
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• pp.449-454
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• 2009

The human face has "expression" as an important visual factor in contrast with general objects. For this reason, cartoonists draw shadow that emphasizes facial shape and facial expression in order to convey atmosphere of scene and trait of character. This shadow should be considered when doing cartoon rendering for facial expression although it is not an physical shading. This paper proposes a cartoon rendering system for facial expression based on shading techniques of real cartoonist. First of all, we searched such techniques of cartoonist through variety of collected cartoon images and defined shadow templates according to character's facial expression to do cartoon rendering diffently. After that, we demonstrated cartoon rendering system of facial expression on the basis of survey result that effectively emphasizes facial shape and facial expression. Finally, we showed the usefulness through the user questionnaire.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.523-528
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• 2015

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing risk situation. The proposed method firstly extracts the facial region from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, discriminates facial expression, and recognizes risk situation. The proposed method is evaluated for Cohn-Kanade database image to recognize facial expression. The DB has 6 kinds of facial expressions of human being that are basic facial expressions such as smile, sadness, surprise, anger, disgust, and fear expression. The proposed method produces good results of facial expression and discriminates risk situation well.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• pp.881-886
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• 2012

The paper is facial expression recognition based on the inclined face geometrical information. In facial expression recognition, mouth has a key role in expressing emotions, in this paper the features is mainly based on the shapes of mouth, followed by eyes and eyebrows. This paper makes its efforts to disperse every feature values via the weighting function and proposes method of expression classification with excellent classification effects; the final recognition model has been constructed.