• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.34-39
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• 2009

As a subjective recognition effect, human's emotion has impulsive characteristic and it expresses intentions and needs unconsciously. These are pregnant with information of the context about the ubiquitous computing environment or intelligent robot systems users. Such indicators which can aware the user's emotion are facial image, voice signal, biological signal spectrum and so on. In this paper, we generate the each result of facial and voice emotion recognition by using facial image and voice for the increasing convenience and efficiency of the emotion recognition. Also, we extract the feature which is the best fit information based on image and sound to upgrade emotion recognition rate and implement Multi-Modal Emotion recognition system based on feature fusion. Eventually, we propose the possibility of the ubiquitous computing service reasoning method based on Bayesian Network and ubiquitous context scenario in the ubiquitous computing environment by using result of emotion recognition.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.155-168
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• 2017

This study investigates how elementary students used cognitive-emotional rebuttals in the context of modeling activities, especially on how their emotional and cognitive processes lead them to use rebuttals in terms of epistemic affect. Twenty-five fifth grade elementary students participated in the study as part of their science class. During the course of their sixth periods, students constructed a human respiratory system model through continuous discussion. The research results showed that elementary students used an elaboration-oriented rebuttal, a defence-oriented rebuttal, and a blame-oriented rebuttal in their modeling activity. The elaboration-oriented rebuttal interspersed with negative epistemic affect was used to elaborate on a student's explanation, and a negative epistemic affect was elicited from their cognitive discrepancy. On the other hand, defence-oriented rebuttal and blame-oriented rebuttal entangled with negative epistemic affect were used to defeat the students rather than help rigor evaluation of students' explanation, and the negative epistemic affect was elicited from the other students' undesirable behavior. These results suggest that students' rebuttals can be elicited by epistemic dynamics related to the epistemic affect. The study shows that if negative epistemic affect were elicited from the other students' naive or false explanations, such an emotion is natural in terms of model construction, and the model can be further developed through the acceptance of the elaboration-oriented rebuttals by students' emotion regulation. In addition, we suggest that negative emotions aroused from the worsening of relationships during small group modeling activities are difficult to regulate and can have negative effects on students' cooperative model construction.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.573-580
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• 2005

In this paper, we propose an emotion recognition using static and dynamic facial images to effectively design human interface. The proposed method is constructed by HMM(Hidden Markov Model), PCA(Principal Component) and wavelet transform. Facial database consists of six basic human emotions including happiness, sadness, anger, surprise, fear and dislike which have been known as common emotions regardless of nation and culture. Emotion recognition in the static images is performed by using the discrete wavelet. Here, the feature vectors are extracted by using PCA. Emotion recognition in the dynamic images is performed by using the wavelet transform and PCA. And then, those are modeled by the HMM. Finally, we obtained better performance result from merging the recognition results for the static images and dynamic images.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.683-687
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• 2010

This paper studied some methods which use frequency warping method that is the one of the speech transformation method to develope the robust speech recognition system for the emotional variation. For this purpose, the effect of emotional variations on the speech signal were studied using speech database containing various emotions and it is observed that speech spectrum is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, new training method that uses frequency warping in training process is presented to reduce the effect of emotional variation and the speech recognition system based on vocal tract length normalization method is developed to be compared with proposed system. Experimental results from the isolated word recognition using HMM showed that new training method reduced the error rate of the conventional recognition system using speech signal containing various emotions.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.821-823
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• 2016

본 논문은 다차원 정서모델 기반 영상, 음성, 뇌파를 이용한 멀티모달 복합 감정인식 시스템을 제안한다. 사용자의 얼굴 영상, 목소리 및 뇌파를 기반으로 각각 추출된 특징을 심리학 및 인지과학 분야에서 인간의 감정을 구성하는 정서적 감응요소로 알려진 다차원 정서모델(Arousal, Valence, Dominance)에 대한 명시적 감응 정도 데이터로 대응하여 스코어링(Scoring)을 수행한다. 이후, 스코어링을 통해 나온 결과 값을 이용하여 다차원으로 구성되는 3차원 감정 모델에 매핑하여 인간의 감정(단일감정, 복합감정)뿐만 아니라 감정의 세기까지 인식한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.325-328
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• 2002

본 논문에서는 사람의 식별과 감정을 인식하기 위한 하나의 방법을 제안한다. 제안된 방법은 색차 정보에 의한 형판의 위치 인식과 형판 벡터 추출에 기반한다. 단일 색차 공간만을 이용할 경우 살색 영역을 정확히 추출하기 힘들다. 이를 보완하기 위해서 여러 가지 색차 공간을 병행하여 살색 영역을 추출하며, 이를 응용하여 각각의 형판을 추출하는 방법을 제안한다. 그리고, 사람의 식별과 감정 인식을 위해서 추출된 형판에 대한 각각의 특징 벡터 추출 방법을 제시하며, 마지막으로 추출된 형판 벡터를 이용하여 신경망을 통한 학습과 인식을 수행하는 방법을 제시한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.191-196
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• 2005

Although the technology for emotion recognition is important one which was demanded in various fields, it still remains as the unsolved problems. Especially, there is growing demand for emotion recognition technology based on racial image. The facial image based emotion recognition system is complex system comprised of various technologies. Therefore, various techniques such that facial image analysis, feature vector extraction, pattern recognition technique, and etc, are needed in order to develop this system. In this paper, we propose new emotion recognition system based un previously studied facial image analysis technique. The proposed system recognizes the emotion by using the fuzzy classifier. The facial image database is built up and the performance of the proposed system is verified by using built database.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.56-57
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• 2013

본 논문에서는 연극공연을 관람하는 관객의 반응정보를 수집하기 위하여, 청각센서를 통해 관객의 음성을 획득하고 획득된 음성에 대한 감정을 예측하여 관객 반응정보 관리시스템에 전송하는 음성신호 기반 감정인식 시스템을 구현한다. 이를 위해, 관객용 헤드셋 마이크와 다채널 녹음장치를 이용하여 관객음성을 획득하는 인터페이스와 음성신호의 특징벡터를 추출하여 SVM (support vector machine) 분류기에 의해 감정을 예측하는 시스템을 구현하고, 이를 관객 반응정보 수집 시스템에 적용한다. 실험결과, 구현된 시스템은 6가지 감정음성 데이터를 활용한 성능평가에서 62.5%의 인식률을 보였고, 실제 연극공연 환경에서 획득된 관객음성과 감정인식 결과를 관객 반응정보 수집 시스템에 전송함을 확인하였다.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2097-2099
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• 2003

본 논문에서는 영상과 음성의 데이터를 이용한 사람의 감정을 인식하는 방법을 제안한다. 제안된 방법은 영상과 음성의 인식률에 기반 한다. 영상이나 음성 중 하나의 출력 데이터만을 이용한 경우에는 잘못된 인식에 따른 결과를 해결하기가 힘들다. 이를 보완하기 위해서 영상과 음성의 출력을 이하여 인식률이 높은 감정 상태에 가중치를 줌으로써 잘못된 인식의 결과를 줄일 수 있는 방법을 제안한다. 이를 위해서는 각각의 감정 상태에 대한 영상과 음성의 인식률이 추출되어져 있어야 하며, 추출된 인식률을 기반으로 가중치를 계산하는 방법을 제시한다.