• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.21-26
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• 2002

This paper presents the new approach of identifying human emotional states such as anger, happiness, normal, sadness, or surprise. This is accomplished by using discrete duration continuous hidden Markov models(DDCHMM). For this, the emotional feature parameters are first defined from input speech signals. In this study, we used prosodic parameters such as pitch signals, energy, and their each derivative, which were then trained by HMM for recognition. Speaker adapted emotional models based on maximum a posteriori(MAP) estimation were also considered for speaker adaptation. As results, the simulation performance showed that the recognition rates of vocal emotion gradually increased with an increase of adaptation sample number.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.305-314
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• 2011

Research into the visual perception of human emotion to recognize an intention has traditionally focused on emotions of facial expression. Recently researchers have turned to the more challenging field of emotional expressions through body posture or activity. Proposed work approaches recognition of basic emotional categories from body postures using neural model applied visual perception of neurophysiology. In keeping with information processing models of the visual cortex, this work constructs a biologically plausible hierarchy of neural detectors, which can discriminate 6 basic emotional states from static views of associated body postures of activity. The proposed model, which is tolerant to parameter variations, presents its possibility by evaluating against human test subjects on a set of body postures of activities.

• Cartoon and Animation Studies
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• s.40
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• pp.477-496
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• 2015

Emotional awareness is the image of a person is represented by different tendencies. Currently, the emotion computing to objectively evaluate the emotion recognition research is being actively studied. However, existing emotional computing research has many problems to run. First, the non-objective in emotion recognition if it is inaccurate. Second, the correlation between the emotion recognition is unclear points. So to test the regularity of image sensitivity to the need of the present study is to control emotions in the computing system. In addition, the screen number of the emotion recognized for the purpose of this study, applying the method of objective image emotional computing system and compared with a similar degree of emotion of the person. The key features of the image emotional computing system calculates the emotion recognized as numbered digital form. And to study the background of emotion computing is a key advantage of the effect of the James A. Russell for digitization of emotion (Core Affect). Pleasure emotions about the core axis (X axis) of pleasure and displeasure, tension (Y-axis) axis of tension and relaxation of emotion, emotion is applied to the computing research. Emotional axis with associated representative sensibility very happy, excited, elated, happy, contentment, calm, relaxing, quiet, tired, helpless, depressed, sad, angry, stress, anxiety, pieces 16 of tense emotional separated by a sensibility ComputingIt applies. Course of the present study is to use the color of the color key elements of the image computing formula sensitivity, brightness, and saturation applied to the sensitivity property elements. Property and calculating the rate sensitivity factors are applied to the importance weight, measured by free-level sensitivity score (X-axis) and the tension (Y-axis). Emotion won again expanded on the basis of emotion crossed point, and included a representative selection in Sensibility size of the top five ranking representative of the main emotion. In addition, measuring the emotional image of a person with 16 representative emotional score, and separated by a representative of the top five senses. Compare the main representative of the main representatives of Emotion and Sensibility people aware of the sensitivity of the results to verify the similarity degree computing emotion emotional emotions depending on the number of representative matches. The emotional similarity computing results represent the average concordance rate of major sensitivity was 51%, representing 2.5 sensibilities were consistent with the person's emotion recognition. Similar measures were the degree of emotion computing calculation and emotion recognition in this study who were given the objective criteria of the sensitivity calculation. Future research will need to be maintained weight room and the study of the emotional equation of a higher concordance rate improved.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.269-276
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• 2022

This study was conducted with the purpose of verifying the effect of the working environment of elderly care workers on service quality and the mediating effect of emotional labor and recognition of filial piety. The subjects of the study were 460 elderly care workers. For the collected data, SPSS Process macro was used. As a result, First, it was found that all variables had a significant positive (+) relationship. Second, the parallel mediating effect of emotional labor and recognition of filial piety was confirmed. Third, the mediating effect of recognition of filial piety and the moderating effect of emotional labor were verified. Based on this study, it was found that filial piety awareness education and emotional labor management are necessary. A follow-up study with a more expanded concept should be conducted.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.214-216
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• 2006

Expressive face and human body gestures are among the main non-verbal communication channels in human-human interaction. Understanding human emotions through body gesture is one of the necessary skills both for humans and also for the computers to interact with their human counterparts. Gesture analysis is consisted of several processes such as detecting of hand, extracting feature, and recognizing emotions. Skin color information for tracking hand gesture is obtained from face detection region. We have revealed relationships between paricular body movements and specific emotions by using HMM(Hidden Markov Model) classifier. Performance evaluation of emotional human body recognition has experimented.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.754-759
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• 2007

In this paper, we proposed the Bi-Modal Sensor Fusion Algorithm which is the emotional recognition method that be able to classify 4 emotions (Happy, Sad, Angry, Surprise) by using facial image and speech signal together. We extract the feature vectors from speech signal using acoustic feature without language feature and classify emotional pattern using Neural-Network. We also make the feature selection of mouth, eyes and eyebrows from facial image. and extracted feature vectors that apply to Principal Component Analysis(PCA) remakes low dimension feature vector. So we proposed method to fused into result value of emotion recognition by using facial image and speech.

• Phonetics and Speech Sciences
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• v.4 no.2
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• pp.73-78
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• 2012

Emotion recognition is an important technology in the filed of human-machine interface. To apply speech technology to emotion recognition, this study aims to establish a relationship between emotional groups and their corresponding voice characteristics by investigating various speech features. The speech features related to speech source and vocal tract filter are included. Experimental results show that statistically significant speech parameters for classifying the emotional groups are mainly related to speech sources such as jitter, shimmer, F0 (F0_min, F0_max, F0_mean, F0_std), harmonic parameters (H1, H2, HNR05, HNR15, HNR25, HNR35), and SPI.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.3-102
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• 2002

$\textbullet$ Human and computer interface(HCI) $\textbullet$ Voice recognition $\textbullet$ Image recognition $\textbullet$ Neural network $\textbullet$ Hopfield net

• Korean Journal of Child Studies
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• v.23 no.3
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• pp.65-78
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• 2002

This study investigated causality in children's emotional intelligence and sociodemographic variables, including family structure, number of siblings, home environment and peer group variables. Subjects were 301 elementary and junior high 11-and 13-year-olds and their mothers. Instruments were questionnaires from the Children's Emotional Intelligence Scale. Data analysis was by Pearson's r, Cronbach's ${\alpha}$, multiple regression and path analysis. Results showed that the variables that affected emotional recognition directly were age, sex, mother's education, economic status of the home, number of siblings, and emotional support of peers. Variables that affected emotion regulation directly were sex, economic status of the home, emotional support of peers, and common activity. Variables that affected emotional facilitation of thinking directly were age, sex, emotional support of peers, and common activity.

• Journal of Families and Better Life
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• v.28 no.3
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• pp.13-26
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• 2010

This study investigates the effects of parent-adolescent communication, emotional intelligence and parentification on the psychological well-being of adolescents. Participants were 712 middle and high school students from Seoul. The collected data were analyzed through a Cronbach's $\alpha$, two-way ANOVA, Pearson's correlation and stepwise multiple regression. The results are as follows: 1) While parent-adolescent communication, parentification, and psychological well-being differed by grade, emotional intelligence did not differ by grade. 2) Father-adolescent communication, regulation of emotion, expression of emotion, practice of emotion, recognition of emotion and mother-adolescent communication were significant predictors of the psychological well-being of middle school students. 3) The recognition of emotion, father-adolescent communication, practice of emotion, regulation of emotion, and mother-adolescent communication were significant predictors of the psychological well-being of high school students. 4) Emotional intelligence played a partially mediating role in the relationship between parent-adolescent communication and psychological well-being.