• Human Ecology Research
• /
• v.52 no.1
• /
• pp.1-10
• /
• 2014

The purpose of this study was to analyse the principles and methods of Taegyo-Singi with regard to emotion and to review basic informations on Taegyo programs for promoting prenatal development. Taegyo-Singi was analyzed as follows. First, the contents of Taegyo-Singi were classified into principles and methods of Taegyo. Second, the domains of emotion were categorized into emotional perception, emotional expression, emotional understanding, and emotional regulation based on the classification of Mayer and Salovey, and Moon. Third, the contents of Taegyo-Singi were classified into the four domains of emotion. Finally, the reliability and validity of the classification were obtained through inter-rater agreement and analysis of content validity. The results indicated that first, the principles of Taegyo presuppose parental influence on temperament formation, and that the emotional states of the mother in the prenatal and prepregnancy period is the most influential variable in a child's temperament formation. Second, the methods of Taegyo presuppose that the human mind interacts with their behavior. Therefore, through emotional support of family members, 'jon-sim (the serene mind)' and 'chung-sim (the mind from rectitude)' are the key methods of Taegyo. This means that the Korean tradition of Taegyo focused on the emotional domain of development, especially emotional regulation. This coincides with the emotion-focused temperament theory that individual differences in temperament reflect individual differences in emotion.

• Journal of Korea Multimedia Society
• /
• v.20 no.8
• /
• pp.1175-1186
• /
• 2017

Human emotion recognition is one of the promising applications in the era of artificial super intelligence. Thus far, facial expression traits are considered to be the most widely used information cues for realizing automated emotion recognition. This paper proposes a novel facial expression recognition (FER) method that works well for recognizing emotion from image sequences. To this end, we develop the so-called weighted soft voting classification (WSVC) algorithm. In the proposed WSVC, a number of classifiers are first constructed using different and multiple feature representations. In next, multiple classifiers are used for generating the recognition result (namely, soft voting) of each face image within a face sequence, yielding multiple soft voting outputs. Finally, these soft voting outputs are combined through using a weighted combination to decide the emotion class (e.g., anger) of a given face sequence. The weights for combination are effectively determined by measuring the quality of each face image, namely "peak expression intensity" and "frontal-pose degree". To test the proposed WSVC, CK+ FER database was used to perform extensive and comparative experimentations. The feasibility of our WSVC algorithm has been successfully demonstrated by comparing recently developed FER algorithms.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.3
• /
• pp.262-267
• /
• 2013

The recognition of human emotional state is one of the most important components for efficient human-human and human- computer interaction. In this paper, four emotions such as fear, disgust, joy, and neutral was a main problem of classifying emotion recognition and an approach of visual-stimuli for eliciting emotion based on physiological signals of skin conductance (SC), skin temperature (SKT), and blood volume pulse (BVP) was used to design the experiment. In order to reach the goal of solving this problem, half-against-half (HAH) multi-class support vector machine (SVM) with Gaussian radial basis function (RBF) kernel was proposed showing the effective techniques to improve the accuracy rate of emotion classification. The experimental results proved that the proposed was an efficient method for solving the emotion recognition problems with the accuracy rate of 90% of neutral, 86.67% of joy, 85% of disgust, and 80% of fear.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.6
• /
• pp.1171-1180
• /
• 2019

Emotion is a psycho-physiological process that plays an important role in human interactions. Affective computing is centered on the development of human-aware artificial intelligence that can understand and regulate emotions. This field of study is also critical as mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction are associated with emotion. Despite the efforts in emotions recognition and emotion detection from nonstationary, detecting emotions from abnormal EEG signals requires sophisticated learning algorithms because they require a high level of abstraction. In this paper, we investigated LSTM hyperparameters for an optimal emotion EEG classification. Results of several experiments are hereby presented. From the results, optimal LSTM hyperparameter configuration was achieved.

• Journal of Oriental Neuropsychiatry
• /
• v.20 no.1
• /
• pp.107-118
• /
• 2009

Objectives : One's mind turned over by environment and personal relationship is Emotion. Emotion is composed by several basic feelings. This basic feeling is called Chiljung in Oriental Medicine. This study is confirmed difference of Chiljung by Constitution. Methods : 80 persons of Oriental medicine university students were tested by Qustionnaire for the Sasang Constitution Classification II(QSCCII) and Thematic Apperception Test(TAT). Constitution is distinguished by QSCCII. 11 pieces picture of TAT were showed one minute and then had person to discribe one's feelings. Two data were analyzed by statistical analysis. Results : Result is as follows, tested TAT about expression of inherent basic emotion. 1. It has relations with cognitive thought process 2. Expression of Chiljung has some relations with constitution and personal trait. Conclusion : Chiljung of inherent basic emotion is cognitive and has difference between constitutions by TAT.

• Journal of IKEEE
• /
• v.13 no.2
• /
• pp.159-166
• /
• 2009

So far, many researches have been done to retrieve music information using static classification descriptors such as genre and mood. Since static classification descriptors are based on diverse content-based musical features, they are effective in retrieving similar music in terms of such features. However, human emotion or mood transition triggered by music enables more effective and sophisticated query in music retrieval. So far, few works have been done to evaluate the effect of human mood transition by music. Using formal representation of such mood transitions, we can provide personalized service more effectively in the new applications such as music recommendation. In this paper, we first propose our Emotion State Transition Model (ESTM) for describing human mood transition by music and then describe a music classification and recommendation scheme based on the ESTM. In the experiment, diverse content-based features were extracted from music clips, dimensionally reduced by NMF (Non-negative Matrix Factorization, and classified by SVM (Support Vector Machine). In the performance analysis, we achieved average accuracy 67.54% and maximum accuracy 87.78%.

• Journal of the Semiconductor & Display Technology
• /
• v.12 no.3
• /
• pp.53-57
• /
• 2013

Human emotions are expressed in various ways. It can be expressed through language, facial expression and gestures. In particular, the facial expression contains many information about human emotion. These vague human emotion appear not in single emotion, but in combination of various emotion. This paper proposes a emotional expression algorithm using Active Appearance Model(AAM) and Fuzz k- Nearest Neighbor which give facial expression in similar with vague human emotion. Applying Mahalanobis distance on the center class, determine inclusion level between center class and each class. Also following inclusion level, appear intensity of emotion. Our emotion recognition system can recognize a complex emotion using Fuzzy k-NN classifier.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.4
• /
• pp.76-81
• /
• 2009

We propose an effective emotion recognition algorithm based on the minimum classification error (MCE) incorporating multi-modal system The emotion recognition is performed based on a Gaussian mixture model (GMM) based on MCE method employing on log-likelihood. In particular, the reposed technique is based on the fusion of feature vectors based on voice signal and galvanic skin response (GSR) from the body sensor. The experimental results indicate that performance of the proposal approach based on MCE incorporating the multi-modal system outperforms the conventional approach.

• Science of Emotion and Sensibility
• /
• v.15 no.1
• /
• pp.97-104
• /
• 2012

Recently, a big text data has been produced by users, an opinion mining to analyze information and opinion about users is becoming a hot issue. Of the opinion mining, especially a sentiment analysis is a study for analysing emotions such as a positive, negative, happiness, sadness, and so on analysing personal opinions or emotions for commercial products, social issues and opinions of politician. To analyze the sentiment analysis, previous studies used a mapping method setting up a distribution of emotions using two dimensions composed of a valence and arousal. But previous studies set up a distribution of emotions arbitrarily. In order to solve the problem, we composed a distribution of 12 emotions through carrying out a survey using Korean emotion words list. Also, certain emotional states on two dimension overlapping multiple emotions, we proposed a selection method with Roulette wheel method using a selection probability. The proposed method shows to classify a text into emotion extracting emotion terms from a text.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.4
• /
• pp.364-371
• /
• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.