• 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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.11a
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• pp.143-147
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• 1998

Autonomic and EEG responses of 38 college students were studied during 60-sec long presentation of International Affective Picture System (IAPS )slides evoking, according to subjective reports, negative (disgust, sadness, surprise) and positive (happiness, exciting) emotional. states. Observed were significant heart rate (HR) deceleration, large skin conductance responses (SCR), moderate respiration frequency slowing, reduction of frontal (F 3, F 4 ) and occipital (O 1, O 2 ) fast alpha, and increases of theta, delta and beta relative spectral power values during the first 30 sec of exposure of IAPS pictures. Analysis carried out to differentiate emotion categories according to autonomic responses indicated that observed HR deceleration was larger in magnitude in surprise and sadness than in disgust, SCR amplitude higher in sadness than in disgust. EEC showed significant differences in theta (F 3, F 4 ) and delta (O 1) power increase in disgust vs. happiness, fast alpha (F 3, F 4 ) power was lower in surprise than in happiness, and slow beta power higher. in happiness than in disgust (0 1). Despite some differences. observed within discrete emotion conditions, overall responses pattern of monitored parameters exhibited similar profiles with few variations, most. obvious. in disgust state, which suggests that affective visual stimulation elicits stereotypical responses in a given passive viewing paradigm. However, the magnitude of physiological responses may vary to certain extent across discrete emotional states making it possible to differentiate among particular experimentally-induced emotional states, e.g., disgust vs. sadness by ANS responses or disgust vs. happiness by EEG measures.

• 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.

• Design Convergence Study
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• v.14 no.4
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• pp.1-16
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• 2015

This study aims to understand distribution of the sentimental words on each genre and find relationship between box office performance and sentimental words in movie review using 673 movies that have more than 1,000 reviews. For the analysis, crawling movie reviews and made data was composed movie genre, movie name, sales, attendance, screen, normal attendance, 7 sentimental words. For analysis results, we used correlation analysis and Parallel coordinates. As a results, First, the highest box office value of the genre is comedy and the lowest box office value of the genre is horror through analyze box office on each genre. Secondly, Movie genre of fantasy feel a lot of boring emotion and Movie genre of SF feel a lot of anger emotion even if 'Happy' and 'Surprise' have highest sentiment value on every genre. Third, We found 'Anger' increase sentimental value when 'Disgust' increase sentimental value and 'Surprise' decrease sentimental value when 'Happy' increase sentimental value through analyze correlation relationship between sentimental words using total data. Fourth, We found 'Happy' have linear relationship between box office and 'Fear' have non-linear relationship between box office through analyze sentimental words according to box office performance.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.63-64
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• 2012

• Korean Institute of Interior Design Journal
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• v.21 no.1
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• pp.103-110
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• 2012

With a theoretical focus on the emotional experiences created via the interface, and the relationship between human beings and space, this study aims to corroborate and clarify the formation and verification of emotional interactions between human beings and space using body movements. It follows the method of Coining "Movement Phrases" through the analysis of body movements in the experimental space, thereby developing them into a complete scenario to produce the story of emotional expression. This study has hereby generated the following outcomes: First, the "pocket-type" exhibition displays a higher frequency of body movements than the "general" exhibition. It has close connections with emotional vocabularies: "Curious," "interesting," "warm," and "fun." The "general" exhibition records a relatively high frequency of emotional vocabularies like "natural," "efficient," and "free." Second, it is possible to analyze the story of space using a scenario, just like drama, based upon attributes and serial relationships. The "exposition" section reveals a high degree of "curiosity" and a large number of body movements, while the "development" section indicates high degree of "surprise" plus slight body movements. The "transition" sections manifest high "interest" and many body movements, and the "climax" section shows a high frequency of "surprise" and many changes in body movements. The "conclusion" section finally invokes images together with body movements.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.122-126
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• 2002

In this study, we find features of 3 emotions (Happiness, Angry, Surprise) as the fundamental research of emotion recognition. Speech signal with emotion has several elements. That is, voice quality, pitch, formant, speech speed, etc. Until now, most researchers have used the change of pitch or Short-time average power envelope or Mel based speech power coefficients. Of course, pitch is very efficient and informative feature. Thus we used it in this study. As pitch is very sensitive to a delicate emotion, it changes easily whenever a man is at different emotional state. Therefore, we can find the pitch is changed steeply or changed with gentle slope or not changed. And, this paper extracts formant features from speech signal with emotion. Each vowels show that each formant has similar position without big difference. Based on this fact, in the pleasure case, we extract features of laughter. And, with that, we separate laughing for easy work. Also, we find those far the angry and surprise.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.05a
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• pp.231-238
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• 2001

Frontal (F3, F4) EEG responses were analyzed and compared during exposure too slides of International Affective Picture System (IAPS) in the study on 42 students. EEG responses during 20 s of exposure to slides intended to elicit happiness (nurturant and erotic), sadness, disgust, surprise, fear or anger emotions were quite similar and were exhibited in theta increase, alpha-blocking and increased beta activity, and frontal asymmetry. However, particular emotions demonstrated variations of the EEG response profiles, enabling to differentiate some pairs of emotions. The profiles showed higher magnitudes of EEG responses in exciting (i.e., erotic happiness) emotion. The most different pairs were exciting -sadness (theta, alpha and alpha asymmetry), exciting-surprise (theta, alpha asymmetry), and exciting-fear (theta, F3 alpha, alpha asymmetry). Nurturant happiness yielded the least differentiation. Differences were found as well within negative emotions, e.g., anger-sadness were differentiated by theta asymmetry, while disgust-fear by beta asymmetry. Obtained results suggest that magnitudes of profiles of EEG variables differentiate emotions elicited by affective pictures.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1958-1962
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• 2005

A driver's emotion is a very important factor of safe driving. This paper classified a driver's emotion into 3 major emotions, can be occur when driving a car: Surprise, Joy, Tired. And It evaluated the classifier using Hidden Markov Models, which have observation sequence as bio-signals. It used the 2-D emotional plane to classfiy a human's general emotion state. The 2-D emotional plane has 2 axes of pleasure-displeasure and arsual-relaxztion. The used bio-signals are Galvanic Skin Response(GSR) and Heart Rate Variability(HRV), which are easy to acquire and reliable. We classified several moving pictures into 3 major emotions to evaluate our HMM system. As a result of driving simulations for each emotional situations, we can get recognition rates of 67% for surprise, 58% for joy and 52% for tired.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.03a
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• pp.11-15
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• 1999

The discrete state theory on emotion postulated that there existed discrete emotions, such as happiness, anger, fear, disgust, and so forth. Many investigators who emphasized discreteness of emotions have suggested that discrete emotions entailed their specific activities in the autonomic nervous system. The purposes of this study were to develop a model of emotion-specific physiological response patterns. The study postulated six emotions (i.e., happiness, sadness, anger, disgust, fear, and surprise) as the basic discrete emotions. Thirty eight college students participated in the present study. Twelve slides (2 for each emotion category) were presented to the subjects in random order. During resting period of 30 s prior to the presentation of each slide, four presentation of each slide, four physiological measures (EEG, ECG, EDA, and respiration) were recorded to establish a baseline. The same physiological measures were recorded while each slide was being presented for 60 s (producing an emotional sate). Then, the subjects were asked to rate the degree of emotion induced by the slide on semantic differential scales. This procedure was repeated for every slide. Based upon the results, a model of emotion-specific physiological response patterns was developed: four emotion (fear, disgust, sadness, and anger) were classified according to the characteristics of EEG and autonomic responses. However, emotions of happiness and surprise were not distinguished by any combination of the physiological measures employed in this study, suggesting another appropriate measure should be adopted for differentiation.