• The Journal of Korea Robotics Society
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• v.5 no.1
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• pp.7-13
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• 2010

This paper presents the sound-based emotion estimation method and the growing HRI (human-robot interaction) system for a Mon-E robot. The method of emotion estimation uses the musical element based on the law of harmony and counterpoint. The emotion is estimated from sound using the information of musical elements which include chord, tempo, volume, harmonic and compass. In this paper, the estimated emotions display the standard 12 emotions including Eckman's 6 emotions (anger, disgust, fear, happiness, sadness, surprise) and the opposite 6 emotions (calmness, love, confidence, unhappiness, gladness, comfortableness) of those. The growing HRI system analyzes sensing information, estimated emotion and service log in an edutainment robot. So, it commands the behavior of the robot. The growing HRI system consists of the emotion client and the emotion server. The emotion client estimates the emotion from sound. This client not only transmits the estimated emotion and sensing information to the emotion server but also delivers response coming from the emotion server to the main program of the robot. The emotion server not only updates the rule table of HRI using information transmitted from the emotion client and but also transmits the response of the HRI to the emotion client. The proposed system was applied to a Mon-E robot and can supply friendly HRI service to users.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.197-203
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• 2005

In this paper, we propose an emotion recognition using facial Images to effectively design human interface. 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 for the facial images is performed after applying the discrete wavelet. Here, the feature vectors are extracted from the PCA and LDA. Experimental results show that human emotions such as happiness, sadness, and anger has better performance than surprise, fear and dislike. Expecially, Japanese shows lower performance for the dislike emotion. Generally, the recognition rates for Korean have higher values than Japanese cases.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.503-516
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• 2012

According to the researches of robot and software agent until now, computational emotion model is dependent on system, so it is hard task that emotion models is separated from existing systems and then recycled into new systems. Therefore, I introduce the Engine of computational Emotion model (shall hereafter appear as EE) to integrate with any robots or agents. This is the engine, ie a software for independent form from inputs and outputs, so the EE is Emotion Generation to control only generation and processing of emotions without both phases of Inputs(Perception) and Outputs(Expression). The EE can be interfaced with any inputs and outputs, and produce emotions from not only emotion itself but also personality and emotions of person. In addition, the EE can be existed in any robot or agent by a kind of software library, or be used as a separate system to communicate. In EE, emotions is the Primary Emotions, ie Joy, Surprise, Disgust, Fear, Sadness, and Anger. It is vector that consist of string and coefficient about emotion, and EE receives this vectors from input interface and then sends its to output interface. In EE, each emotions are connected to lists of emotional experiences, and the lists consisted of string and coefficient of each emotional experiences are used to generate and process emotional states. The emotional experiences are consisted of emotion vocabulary understanding various emotional experiences of human. This study EE is available to use to make interaction products to response the appropriate reaction of human emotions. The significance of the study is on development of a system to induce that person feel that product has your sympathy. Therefore, the EE can help give an efficient service of emotional sympathy to products of HRI, HCI area.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.1-6
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• 2005

In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.148-154
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• 2021

With the advent of context-aware computing, many attempts were made to understand emotions. Among these various attempts, Speech Emotion Recognition (SER) is a method of recognizing the speaker's emotions through speech information. The SER is successful in selecting distinctive 'features' and 'classifying' them in an appropriate way. In this paper, the performances of SER using neural network models (e.g., fully connected network (FCN), convolutional neural network (CNN)) with Mel-Frequency Cepstral Coefficients (MFCC) are examined in terms of the accuracy and distribution of emotion recognition. For Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, by tuning model parameters, a two-dimensional Convolutional Neural Network (2D-CNN) model with MFCC showed the best performance with an average accuracy of 88.54% for 5 emotions, anger, happiness, calm, fear, and sadness, of men and women. In addition, by examining the distribution of emotion recognition accuracies for neural network models, the 2D-CNN with MFCC can expect an overall accuracy of 75% or more.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3498-3512
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• 2021

This article describes how events that make up text stories can be represented and extracted. We also address the results from our simple experiment on extracting and clustering events in terms of emotions, under the assumption that different emotional events can be associated with the classified clusters. Each emotion cluster is based on Plutchik's eight basic emotion model, and the attributes of the NLTK-VADER are used for the classification criterion. While comparisons of the results with human raters show less accuracy for certain emotion types, emotion types such as joy and sadness show relatively high accuracy. The evaluation results with NRC Word Emotion Association Lexicon (aka EmoLex) show high accuracy values (more than 90% accuracy in anger, disgust, fear, and surprise), though precision and recall values are relatively low.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.105-110
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• 2008

Human being recognizes emotion fusing information of the other speech signal, expression, gesture and bio-signal. Computer needs technologies that being recognized as human do using combined information. In this paper, we recognized five emotions (normal, happiness, anger, surprise, sadness) through speech signal and facial image, and we propose to method that fusing into emotion for emotion recognition result is applying to multimodal method. Speech signal and facial image does emotion recognition using Principal Component Analysis (PCA) method. And multimodal is fusing into emotion result applying fuzzy membership function. With our experiments, our average emotion recognition rate was 63% by using speech signals, and was 53.4% by using facial images. That is, we know that speech signal offers a better emotion recognition rate than the facial image. We proposed decision fusion method using S-type membership function to heighten the emotion recognition rate. Result of emotion recognition through proposed method, average recognized rate is 70.4%. We could know that decision fusion method offers a better emotion recognition rate than the facial image or speech signal.

• Journal of Korean Medical classics
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• v.22 no.2
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• pp.315-325
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• 2009

Since the days of early Confucianism, the nature(性) and the emotion(情) have been the methodological tool to explain the original nature of the human being, by dividing the human mind into the nature and the emotion. This study examines the interconversion(相成相資) theory of the nature and the emotion mentioned in the "Sadanron(四端論)" chapter of "Dong-uisusebowon." We have contemplated how Gi(氣) of sadness and anger mutually create each other, that of pleasure and enjoyment do as well, transforming sad nature[哀性] of the Taeyang-in(太陽人) into anger emotion, anger nature of the Soyang-in(少陽人) into sad emotion[哀情], pleasure nature[喜性] of the Taeuemin(太陰人) into enjoy emotion[樂情], enjoy nature[樂性] of the Soeumin(少陰人) into pleasure emotion[喜情] considering the circumstances in which the nature changes into the emotion based on the original texts of "Dong-uisusebowon", "Sasang-uihakchobon-gwon(四象醫學草本卷)" and "Gyeokchigo(格致藁)".

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.13-20
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• 2019

This study aims to study the codes that can code the Sijo's emotional codes into AI and use them in literature therapy. In this study, we conducted emotional coding of the Sijo Crying Cuckoo at the Empty Mountain. As a result, the Emotion Codon was able to indicate the state of sadness catharsis. This implanting of the Sijo's emotional codes into Emotion Codon is like implanting human emotions into AI. If the basic emotion codes are implanted in the Emotion Codon and induced of AI's self-learning, We think AI can combine various emotions that occur in the human body. AI can then replace human emotions, which can be useful in treating of human emotions. It is believed that continuing this study will induce human emotions to heal the mind and spirit.