• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.328-333
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• 2003

This paper did to do with 18 muscle pairs that do fetters in anatomy that influence in facial expression change and mix motion of muscle for face facial animation. After set and change mash and make standard model in individual's image, did mapping to mash using individual facial front side and side image to raise truth stuff. Muscle model who become motive power that can do animation used facial expression creation correcting Waters' muscle model. Created deformed face that texture is dressed using these method. Also, 6 facial expression that Ekman proposes did animation.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.7-9
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• 2015

Purpose In the many face-related application such as head pose estimation, 3D face modeling, facial appearance manipulation, the robust and fast facial feature extraction is necessary. We present the facial feature extraction method based on shape regression and feature selection for real-time facial feature extraction. Materials and Methods The facial features are initialized by statistical shape model and then the shape of facial features are deformed iteratively according to the texture pattern which is selected on the feature pool. Results We obtain fast and robust facial feature extraction result with error less than 4% and processing time less than 12 ms. The alignment error is measured by average of ratio of pixel difference to inter-ocular distance. Conclusion The accuracy and processing time of the method is enough to apply facial feature based application and can be used on the face beautification or 3D face modeling.

• Journal of Information Processing Systems
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• v.6 no.2
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• pp.261-268
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• 2010

Tracking human facial expression within a video image has many useful applications, such as surveillance and teleconferencing, etc. Initially, the Active Appearance Model (AAM) was proposed for facial recognition; however, it turns out that the AAM has many advantages as regards continuous facial expression recognition. We have implemented a continuous facial expression recognition system using the AAM. In this study, we adopt an independent AAM using the Inverse Compositional Image Alignment method. The system was evaluated using the standard Cohn-Kanade facial expression database, the results of which show that it could have numerous potential applications.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1657-1662
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• 2017

Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3685-3700
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• 2015

To avoid influences caused by pose, illumination and facial expression variations, we propose a robust three-step algorithm based on ASM and POEM for facial landmark localization. Firstly, Model Selection Factor is utilized to achieve a pose-free initialized shape. Then, we use the global shape model of ASM to describe the whole face and the texture model POEM to adjust the position of each landmark. Thirdly, a second localization is presented to discriminatively refine the subtle shape variation for some organs and contours. Experiments are conducted in four main face datasets, and the results demonstrate that the proposed method accurately localizes facial landmarks and outperforms other state-of-the-art methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.562-567
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• 2009

As a key mechanism of the human emotion interaction, Facial Expression is a powerful tools in HRI(Human Robot Interface) such as Human Computer Interface. By using a facial expression, we can bring out various reaction correspond to emotional state of user in HCI(Human Computer Interaction). Also it can infer that suitable services to supply user from service agents such as intelligent robot. In this article, We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.

• Archives of Craniofacial Surgery
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• v.12 no.2
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• pp.86-92
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• 2011

Purpose: The purpose of this study was to investigate the possibility that a dynamic facial composite flap with sensory and motor nerves could be made available from donor facial composite tissue. Methods: The faces of 3 human cadavers were dissected. The authors studied the donor faces to assess which facial composite model would be most practicable. A "panorama facial flap" was excised from each facial skeleton with circumferential incision of the oral mucosa, lower conjunctiva and endonasal mucosa. In addition, the authors measured the available length of the arterial and venous pedicles, and the sensory nerves. In the recipient, the authors evaluated the time required to anastomose the vessels and nerve coaptations, anchor stitches for donor flaps, and skin stitches for closure. Results: In the panorama facial flap, the available anastomosing vessels were the facial artery and vein. The sensory nerves that required anastomoses were the infraorbital nerve and inferior alveolar nerve. The motor nerve requiring anstomoses was the facial nerve. The vascular pedicle of the panorama facial flap is the facial artery and vein. The longest length was 78 mm and 48 mm respectively. Sensation of the donor facial composite is supplied by the infraorbital nerve and inferior alveolar nerve. Motion of the facial composite is supplied by the facial nerve. Some branches of the facial nerve can be anastomosed, if necessary. Conclusion: The most practical facial composite flap would be a mid and lower face flap, and we proposed a panorama facial flap that is designed to incorporate the mid and lower facial skin with and the unique tissue of the lip. The panorama facial composite flap could be considered as one of the practicable basic models for facial allotransplantation.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.10
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• pp.957-962
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• 2010

If we want to recognize the human's emotion via the facial image, first of all, we need to extract the emotional features from the facial image by using a feature extraction algorithm. And we need to classify the emotional status by using pattern classification method. The AAM (Active Appearance Model) is a well-known method that can represent a non-rigid object, such as face, facial expression. The Bayesian Network is a probability based classifier that can represent the probabilistic relationships between a set of facial features. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with FACS (Facial Action Coding System) for automatically modeling and extracting the facial emotional features. To recognize the facial emotion, we use the DBNs (Dynamic Bayesian Networks) for modeling and understanding the temporal phases of facial expressions in image sequences. The result of emotion recognition can be used to rehabilitate based on biofeedback for emotional disabled.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1128-1141
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• 2009

Graphical expressions are continuously improving, spurred by the astonishing growth of the game technology industry. Despite such improvements, users are still demanding a more natural gaming environment and true reflections of human emotions. In real life, people can read a person's moods from facial color and expression. Hence, interactive facial colors in game characters provide a deeper level of reality. In this paper we propose a facial color adaptive technique, which is a combination of an emotional model based on human emotion theory, emotional expression pattern using colors of animation contents, and emotional reaction speed function based on human personality theory, as opposed to past methods that expressed emotion through blood flow, pulse, or skin temperature. Experiments show this of expression of the Facial Color Model based on facial color adoptive technique and expression of the animation contents is effective in conveying character emotions. Moreover, the proposed Facial Color Adaptive Technique can be applied not only to 2D games, but to 3D games as well.