• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.282-287
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• 2007

Humanoid and android robots are emerging as a trend shifts from industrial robot to personal robot. So human-robot interaction will increase. Ultimate objective of humanoid and android would be a robot like a human. In this aspect, implementation of robot's facial expression is necessary in making a human-like robot. This paper proposes a dynamic emotion model for a mascot-type robot to display similar facial and more recognizable expressions.

• Korean Journal of Cognitive Science
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• v.18 no.1
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• pp.57-68
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• 2007

This study explores children's categories of emotion understanding from facial expressions onto two dimensional structure of emotion. Children of 89 from 3 to 5 years old were required to those facial expressions related the fourteen emotion terms. Facial expressions applied for experiment are used the photographs rated the degree of expression in each of the two dimensions (pleasure-displeasure dimension and arousal-sleep dimension) on a nine-point scale from 54 university students. The experimental results showed that children indicated the greater stability in arousal dimension than stability in pleasure-displeasure dimension. Emotions about sadness, sleepiness, anger and surprise onto two dimensions was understand very well, but emotions about fear, boredom were showed instability in pleasure-displeasure dimension. Specifically, 3 years old children indicated highly the perception in a degree of arousal-sleep than perception of pleasure-displeasure.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.7-15
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• 2014

This paper proposed a recognition algorithm of human facial expressions using the PCA and the template matching. Firstly, face image is acquired using the Haar-like feature mask from an input image. The face image is divided into two images. One is the upper image including eye and eyebrow. The other is the lower image including mouth and jaw. The extraction of facial components, such as eye and mouth, begins getting eye image and mouth image. Then an eigenface is produced by the PCA training process with learning images. An eigeneye and an eigenmouth are produced from the eigenface. The eye image is obtained by the template matching the upper image with the eigeneye, and the mouth image is obtained by the template matching the lower image with the eigenmouth. The face recognition uses geometrical properties of the eye and mouth. The simulation results show that the proposed method has superior extraction ratio rather than previous results; the extraction ratio of mouth image is particularly reached to 99%. The face recognition system using the proposed method shows that recognition ratio is greater than 80% about three facial expressions, which are fright, being angered, happiness.

• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.127-143
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• 2010

This study explores children's sex differences of emotion discrimination from facial expressions based on two dimensional model of emotion. The study group consisted of 92 children, of 40, 52, and 64 months of age, and the rate of male and female children was male children (50%) and female children (50%). Children of 92 were required to choose facial expressions related the twelve emotion terms. Facial expressions applied for experiment are used the photographs rated the degree of expression in each of the two dimensions (pleasure-displeasure dimension and arousal-sleep dimension) on a nine-point scale from 54 university students. The experimental findings appeared that the sex differences were distinctly the arousal-sleep dimension than the pleasure-displeasure dimension. In the arousal-sleep dimensionoussleepness, anger, comfort, and loneliness' emotions showed large sex differences over 1 value. Especially, while male children showed high arousal more than female children in the emotions like 'sleepiness, anger and loneliness', female children showed high arousal more than male children in 'comfort' emotion.

• Korean Journal of Cognitive Science
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• v.14 no.1
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• pp.41-50
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• 2003

We tested the capability of the pleasure and arousal dimension-based facial expression synthesis system and proposed the most effective interface for it. First, we tried to confirm the adequateness of the dimensional model as a basic structure of the internal states for the system. Fer it, subjects compared the 17 facial expressions on the two axes. The results validated the fundamental hypothesis of the system. Second, we chose 21 representative expressions from the system to test its performance and had subjects rate their similarities. We analyzed these data using multidimensional scaling methods and these results verified the system's reliability. Third, we compared the efficiencies of two interfaces -coordinate values and slide bars- to find the most suitable interface for the system. Subjects synthesise 25 facial expressions with each interface of it. The results showed that the visualization of two dimensional values into Cartesian coordinate is more stable as an input display of facial expression synthesis system based on dimensions.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.70-80
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• 2013

The purpose of this study has placed its meaning in the use as the basic material for the research of the person's facial expressions, by researching and analyzing the visual reactions of recognition of children according to the facial expressions of emotion and by surveying the verbal reactions of boys and girls according to the individual expressions of emotion. The subjects of this study were 108 children at the age of 6 - 8 (55 males, 53 females) who were able to understand the presented research tool, and the response survey conducted twice were used in the method of data collection by individual interviews and self administered questionnaires. The research tool using in the questionnaires were classified into 6 types of joy, sadness, anger, surprise, disgust, and fear which could derive the specific and accurate responses. Regarding children's visual reactions of recognition, both of boys and girls showed the high frequency in the facial expressions of joy, sadness, anger, surprise, and the low frequency in fear, disgust. Regarding verbal reactions, it showed the high frequency in the heuristic responses either to explore or the responds to the impressive parts reminiscent to the facial appearances in all the joy, sadness, anger, surprise, disgust, fear. And it came out that the imaginary responses created new stories reminiscent to the facial expression in surprise, disgust, and fear.

• Journal of Korean Academy of Nursing
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• v.20 no.3
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• pp.381-398
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• 1990

Nursing involves deep human interpersonal relationships between nurses and patients. But in modem Korea, the nurse - patient relationship tends to be ritualistic and mechanestic. Patients usually express the hope that nurses be more tender and kind. Patients expect nurses to express their warmth especially through nonverbal behaviour. This study was conducted to identify patients' preferences for nurse's nonverbal expressions of warmth. Through the confirmation of these preferences, nurses may learn how to enhance their interpersonal relationships with patients. Subjects for the study were 73 patients who had been admitted to a university teaching hospital for at least three days and agreed to be interviewed by the investigator. The interactions were studied nonverbal expressions of warmth during nursing rounds and administration of oral medication. The interview schedule was expecially designed by the investigator to measure the nurse's posture, the distance between the nurse and the patient, the nurse's eye contact, facial expression, hand motion and head nodding. Data analysis included frequencies, percentages and X²-test. The results of this study may be summerized as follows : 1. Patient's preferences for nurse's nonverbal expressions of warmth during nursing rounds. Preferred nurse's posture was sitting(50.7%) or standing(49.3%) opposite the patient. Preferred distance between the nurse and the patient was close to the bed(93.2%), less than 1m. Preferred eye contact was directed to the patient's eyes or their affected part (41.1%). Preferred facial expression was a smile(97.3%). Preferred hand motions were light gestures(41.1%). Patients preferred head nodding which approved their own opinions(69.9%). 2. Patient's preferences for nurse's nonverval expressions of warmth during administration of oral medication. Preferred nurse's posture was standing and waiting to confirm that the medication had been taken(58.9%). Preferred distance from the patient was at arm's length, 0.5-1m(64.4%). Patients preferred direct eye contact(58.9%) and a smile(94.5%). Patients preferred that the nurse put the medicine directly the patient's hand(64.4%). Whether the nurse nodded her head or not was not considered important. 3. The relation of general characteristics and patient's preferences for nurse's nonverbal expressions of warmth during nursing rounds and administration of oral medication. During nursing rounds, the age of subjects(p＝0.010) and the standard of education(p＝0.026) related to the distance between the nurse and the patient. The sick hospital ward related to the eye contact(p＝0.017) and facial expression(p＝0.010). During administration of oral medication, the age of subjects(p＝0.044) and days of hospital treatment (p＝0.043) and the sick hospital ward(p＝0.0004) related to the facial expression. From this study, nurses can learn what kind nonverbal expressions of warmth are preferred by patients during rounds and administration and thus will enhance nurse- patient interpersonal relationships.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.16.1-164
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• 1998

This paper describes Facial Expression Explorer to search for the components of a facial expression and to map the expression to other expressionless figures like a robot, frog, teapot, rabbit and others. In general, it is a time-consuming and laborious job to create a facial expression manually, especially when the facial expression must personify a well-known public figure or an actor. In order to extract a blending ratio from facial images automatically, the Facial Expression Explorer uses Networked Genetic Algorithm(NGA) which is a fast method for the convergence by GA. The blending ratio is often used to create facial expressions through shape blending methods by animators. With the Facial Expression Explorer a realistic facial expression can be modeled more efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.523-528
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• 2015

This paper proposes an algorithm for risk situation recognition using facial expression. The proposed method recognitions the surprise and fear expression among human's various emotional expression for recognizing risk situation. The proposed method firstly extracts the facial region from input, detects eye region and lip region from the extracted face. And then, the method applies Uniform LBP to each region, discriminates facial expression, and recognizes risk situation. The proposed method is evaluated for Cohn-Kanade database image to recognize facial expression. The DB has 6 kinds of facial expressions of human being that are basic facial expressions such as smile, sadness, surprise, anger, disgust, and fear expression. The proposed method produces good results of facial expression and discriminates risk situation well.