• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.282-287
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• 2006

This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.647-652
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive Feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.666-670
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.322-327
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• 2007

Researchers in the field of psychology used Electroencephalographic (EEG) to record activities of human brain lot many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study emotion recognition method which uses one of physiological signals and gestures in the existing research. In this paper, we use together physiological signals and gestures for emotion recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both physiological signals and gestures gets high recognition rates better than using physiological signals or gestures. Both physiological signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.832-837
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.25-28
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• 2007

Electroencephalograhic(EEG)는 심리학의 영역에서 인간 두뇌의 활동을 측정 기록하는데 오래 전부터 사용하였다. 과학의 발달함에 따라 점차적으로 인간의 두뇌에서 감정을 조절하는 기본적인 영역들이 밝혀지고 있다. 그래서 인간의 감정을 조절하는 인간의 두뇌 활동 영역들을 EEG를 이용하여 측정하였다. 본 논문에서는 EEG의 신호들과 몸짓을 이용해서 감정을 인식하였다. 특히, 기존에 생체신호나 몸짓 중 한 가지만을 이용하여 각각 실험해서 감성을 인식하였지만, 본 논문에서는 EEG 신호와 몸짓을 동시에 이용해서 피 실험자의 감성을 인식하는 실험을 하였다. 실험결과 기존의 생체신호나 몸짓 한 가지만을 가지고 실험했을 때의 인식률 보다 더 높은 인식률을 보임을 알 수 있었다. 그리고 생체신호와 몸짓들의 특징 신호들은 강화학습의 개념을 이용한 IFS(Interactive Feature Selection)를 이용하여 특징 선택을 하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.256-261
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology developed, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on the reinforcement learning.