• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.383-390
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• 2004

This paper propose a method that controls facial expression of 3D avatar by having the user select a sequence of facial expressions in the space of facial expressions. And we setup its system. The space of expression is created from about 2400 frames consist of motion captured data of facial expressions. To represent the state of each expression, we use the distance matrix that represents the distances between pairs of feature points on the face. The set of distance matrices is used as the space of expressions. But this space is not such a space where one state can go to another state via the straight trajectory between them. We derive trajectories between two states from the captured set of expressions in an approximate manner. First, two states are regarded adjacent if the distance between their distance matrices is below a given threshold. Any two states are considered to have a trajectory between them If there is a sequence of adjacent states between them. It is assumed . that one states goes to another state via the shortest trajectory between them. The shortest trajectories are found by dynamic programming. The space of facial expressions, as the set of distance matrices, is multidimensional. Facial expression of 3D avatar Is controled in real time as the user navigates the space. To help this process, we visualized the space of expressions in 2D space by using the multidimensional scaling(MDS). To see how effective this system is, we had users control facial expressions of 3D avatar by using the system. As a result of that, users estimate that system is very useful to control facial expression of 3D avatar in real-time.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.83-94
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• 2011

Main researching issue in affective computing is to give a machine the ability to recognize the emotion of a person and to react it properly. Efforts in that direction have mainly focused on facial and oral cues to get emotions. Postures have been recently considered as well. This paper aims to discriminate emotions posture by identifying and measuring the saliency of posture features that play a role in affective expression. To do so, affective postures from human subjects are first collected using a motion capture system, then emotional features in posture are described with spatial ones. Through standard statistical techniques, we verified that there is a statistically significant correlation between the emotion intended by the acting subjects, and the emotion perceived by the observers. Discriminant Analysis are used to build affective posture predictive models and to measure the saliency of the proposed set of posture features in discriminating between 6 basic emotional states. The evaluation of proposed features and models are performed using a correlation between actor-observer's postures set. Quantitative experimental results show that proposed set of features discriminates well between emotions, and also that built predictive models perform well.

• Journal of Korea Game Society
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• v.15 no.4
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• pp.189-198
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• 2015

This paper is about a serious game controlled by EEG and motion capture. We developed our game for 2 users competitive and its method is as follows. One player uses a controlling interface using EEG signals based on the premise that the player's facial movements are a depiction of the player's emotion and intensity throughout the game play. The other player uses a controlling interface using kinect's motion capture technology which captures the player's vertical and lateral movements as well as state of running. The game shows the first player's EEG as a real-time graphic along the map on the game screen. The player will then be able to pace himself based on these visualization graphics of his brain activities. This results in higher concentration for the player throughout the game for a better score in the game. In addition, the second player will be able to improve his physical abilities since the game action is based on real movements from the player.

• Proceedings of the ESK Conference
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• 1996.10a
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• pp.59-65
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• 1996

인간의 얼굴 표정은 인간의 감성이 가장 잘 나타나는 부분이다 . 따라서 전통적으로 인간의 표정을 감 성과 연관 지어 연구하려는 많은 노력이 있어 왔다. 최근에는 얼굴 온도 변화를 측정하는 방법, 근전도(EMG; Electromyography)로 얼굴 근육의 움직임을 측정하는 방법, 이미지나 동작분석에 의한 얼굴 표정의 연구가 가능 하게 되었다. 본 연구에서는 인간의 얼굴 표정 변화를 3차원 동작분석 장비를 이용하여 측정하였다. 얼굴 표정 의 측정을 위해 두가지의 실험을 계획하였는데, 첫번 째 실험에서는 피실험자들로 하여금 웃는 표정, 놀라는 표정, 화난 표정, 그리고 무표정 등을 짓게 한 후 이를 측정하였으며, 두번째 실험에스는 코미디 영화와 공포 영화를 피 실험자들에게 보여 주어 피실험자들의 표정 변화를 측정하였다. 5명의 성인 남자가 실험에 참여하였는데, 감성을 일으킬 수 있는 적절한 자극 제시를 못한 점 등에서 처음에 기도했던 6개의 기본 표정(웃음, 슬픔, 혐오, 공포, 화남, 놀람)에 대한 모든 실험과 분석이 수행되지 못했다. 나머지 부분을 포함한 정교한 실험 준비가 추후 연구 에서 요구된다. 이러한 연구는 앞으로 감성공학, 소비자 반응 측정, 컴퓨터 애니메이션(animation), 정보 표시 장치(display) 수단으로서 사용될 수 있을 것이다.

• The Journal of the Korea institute of electronic communication sciences
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• v.4 no.2
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• pp.116-122
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• 2009

In this paper, we propose a new method which generates the trajectory of the mouth shape for the characters by the user inputs. It is based on the character at a basis syllable and can be suitable to the mouth shape generation. In this paper, we understand the principle of the Korean language creation and find the similarity for the form of the mouth shape and select it as a basic syllable. We also consider the articulation of this phoneme for it and create a new mouth shape trajectory and apply at face of an 3D avatar.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.19-29
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• 2006

As the security of personal information is becoming more important in mobile phones, we are starting to apply iris recognition technology to these devices. In conventional iris recognition, magnified iris images are required. For that, it has been necessary to use large magnified zoom & focus lens camera to capture images, but due to the requirement about low size and cost of mobile phones, the zoom & focus lens are difficult to be used. However, with rapid developments and multimedia convergence trends in mobile phones, more and more companies have built mega-pixel cameras into their mobile phones. These devices make it possible to capture a magnified iris image without zoom & focus lens. Although facial images are captured far away from the user using a mega-pixel camera, the captured iris region possesses sufficient pixel information for iris recognition. However, in this case, the eye region should be detected for accurate iris recognition in facial images. So, we propose a new fast iris detection method, which is appropriate for mobile phones based on corneal specular reflection. To detect specular reflection robustly, we propose the theoretical background of estimating the size and brightness of specular reflection based on eye, camera and illuminator models. In addition, we use the successive On/Off scheme of the illuminator to detect the optical/motion blurring and sunlight effect on input image. Experimental results show that total processing time(detecting iris region) is on average 65ms on a Samsung SCH-S2300 (with 150MHz ARM 9 CPU) mobile phone. The rate of correct iris detection is 99% (about indoor images) and 98.5% (about outdoor images).

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.461-470
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• 2023

While speech animation generation employing deep learning has been actively researched for English, there has been no prior work for Korean. Given the fact, this paper for the very first time employs supervised deep learning to generate Korean speech animation. By doing so, we find out the significant effect of deep learning being able to make speech animation research come down to speech recognition research which is the predominating technique. Also, we study the way to make best use of the effect for Korean speech animation generation. The effect can contribute to efficiently and efficaciously revitalizing the recently inactive Korean speech animation research, by clarifying the top priority research target. This paper performs this process: (i) it chooses blendshape animation technique, (ii) implements the deep-learning model in the master-servant pipeline of the automatic speech recognition (ASR) module and the facial action coding (FAC) module, (iii) makes Korean speech facial motion capture dataset, (iv) prepares two comparison deep learning models (one model adopts the English ASR module, the other model adopts the Korean ASR module, however both models adopt the same basic structure for their FAC modules), and (v) train the FAC modules of both models dependently on their ASR modules. The user study demonstrates that the model which adopts the Korean ASR module and dependently trains its FAC module (getting 4.2/5.0 points) generates decisively much more natural Korean speech animations than the model which adopts the English ASR module and dependently trains its FAC module (getting 2.7/5.0 points). The result confirms the aforementioned effect showing that the quality of the Korean speech animation comes down to the accuracy of Korean ASR.