• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.132-134
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• 2004

Emotion recognition between human and human is done compositely using various features that are face, voice, gesture and etc. Among them, it is a face that emotion expression is revealed the most definitely. Human expresses and recognizes a emotion using complex and various features of the face. This paper proposes hybrid feature extraction for emotions recognition from facial expression. Hybrid feature extraction imitates emotion recognition system of human by combination of geometrical feature based extraction and color distributed histogram. That is, it can robustly perform emotion recognition by extracting many features of facial expression.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2011.11a
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• pp.104-105
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• 2011

• Design Convergence Study
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• v.16 no.6
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• pp.137-152
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• 2017

This research aims to develop the companion robot experience design for elderly in korea based on needs-function deploy matrix of robot and emotion expression research of robot in multimodal interaction. First, Elder users' main needs were categorized into 4 groups based on ethnographic research. Second, the functional elements and physical actuators of robot were mapped to user needs in function- needs deploy matrix. The final UX design prototype was implemented with a robot type that has a verbal non-touch multi modal interface with emotional facial expression based on Ekman's Facial Action Coding System (FACS). The proposed robot prototype was validated through a user test session to analyze the influence of the robot interaction on the cognition and emotion of users by Story Recall Test and face emotion analysis software; Emotion API when the robot changes facial expression corresponds to the emotion of the delivered information by the robot and when the robot initiated interaction cycle voluntarily. The group with emotional robot showed a relatively high recall rate in the delayed recall test and In the facial expression analysis, the facial expression and the interaction initiation of the robot affected on emotion and preference of the elderly participants.

• Journal of the Korean Home Economics Association
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• v.40 no.10
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• pp.103-112
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• 2002

This study investigated the relation between preschoolers' emotion understanding and parents' emotion expressiveness and attitude toward children's emotion expressiveness. Subjects were ninety 3- to 5-year old children and their parents. Parents' emotion socialization was measured by PACES developed by Saami(1989) and FEQ developed by Harberstadt(1986). And preschoolers' identification of basic emotional expressions and expression of their own feelings and others' feelings in various situations were measured. Results revealed that 5-year-old children understood emotion better than 3-year-old children, and mother's positive emotion expression influenced children's emotion understanding. The results are consistent with recent research showing that parents emotion socialization may be important for preschoolers' emotion understanding.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.371-376
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• 2007

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

• Korean Journal of Child Studies
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• v.21 no.4
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• pp.21-34
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• 2000

This investigation of developmental trends in children's emotional intelligence used the Emotional Intelligence Rating Scale for Preschool Children(Kim, 1998) to study 973 children. Significant age differences were found in 5 factors: 'utilization of emotion', 'regulation of emotion', 'handling of relationship between child and teacher' and 'handling of relationship with peers'. Children's emotional intelligence scores increased with age in 3 factors: 'utilization of emotion', 'empathy', and 'regulation of emotion'. Sex differences were found in 5 factors: 'utilization of emotion', 'empathy', 'appraisal and expression of self emotion', 'regulation of emotion', and 'handling of relationship between child and teacher'. In all factors, the scores of girls were higher than those the scores of boys.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.295-305
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• 2021

This study identifies the relationship between the anger expression and interpersonal problems of college students, and then confirms the influence on emotion regulation difficulties as a mediating effect. And, it was conducted to understand college students who complain of interpersonal problems and to provide basic data for developing programs that can solve their problems. As a result of the study, anger expression showed a positive correlation in interpersonal relations problem and emotion regulation difficulty, and interpersonal relations problem showed somewhat high positive correlation with emotion regulation difficulty. In addition, it was found that emotion control difficulties were partially mediated in the anger expression among college students and interpersonal relationships. In conclusion, we could see the need for future research to develop educational programs that reduce the difficulty of emotion control and lead to positive anger expression among college students, and intervention programs that can improve interpersonal relationships.

• Journal of the HCI Society of Korea
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• v.9 no.1
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• pp.23-28
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• 2014

In this study we focus on the effect of the interaction between humans and reactive television when emotion recognition through facial expression mechanism is used. Most of today's user interfaces in electronic products are passive and are not properly fitted into users' needs. In terms of the user centered device, we propose that the emotion based reactive television is the most effective in interaction compared to other passive input products. We have developed and researched next generation cognitive TV models in user centered. In this paper we present a result of the experiment that had been taken with Fraunhofer IIS $SHORE^{TM}$ demo software version to measure emotion recognition. This new approach was based on the real time cognitive TV models and through this approach we studied the relationship between humans and cognitive TV. This study follows following steps: 1) Cognitive TV systems can be on automatic ON/OFF mode responding to motions of people 2) Cognitive TV can directly select channels as face changes (ex, Neutral Mode and Happy Mode, Sad Mode, Angry Mode) 3) Cognitive TV can detect emotion recognition from facial expression of people within the fixed time and then if Happy mode is detected the programs of TV would be shifted into funny or interesting shows and if Angry mode is detected it would be changed to moving or touching shows. In addition, we focus on improving the emotion recognition through facial expression. Furthermore, the improvement of cognition TV based on personal characteristics is needed for the different personality of users in human to computer interaction. In this manner, the study on how people feel and how cognitive TV responds accordingly, plus the effects of media as cognitive mechanism will be thoroughly discussed.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1175-1186
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• 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 Digital Convergence
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• v.17 no.12
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• pp.289-296
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• 2019

This study was done to explore the relationship alcoholcs' body image, belief emotion expression, communication competence and related factors. Alcoholcs' body image, belief emotion expression, communication competence level scales were used as the measurement tools with a sample of 151 students from 2 hospitals in J-city and S-city. T-test, ANOVA, Pearson correlation and stepwise multiple regression with SPSS/WIN 21.0 version were used to analyze the data. Alcoholcs' body-image was negatively correlated with belief emotion expression but was negatively correlated with communication competence. Belief emotion expression was negatively correlated with communication competence. Hierarchical regression analysis showed that body-image, education degree, and belief emotion expression explained 24.9 % of communication competence. The results of this study can be used as basic data to improve the communication competence of alcoholics.