• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.573-580
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• 2005

In this paper, we propose an emotion recognition using static and dynamic facial images to effectively design human interface. The proposed method is constructed by HMM(Hidden Markov Model), PCA(Principal Component) and wavelet transform. Facial database consists of six basic human emotions including happiness, sadness, anger, surprise, fear and dislike which have been known as common emotions regardless of nation and culture. Emotion recognition in the static images is performed by using the discrete wavelet. Here, the feature vectors are extracted by using PCA. Emotion recognition in the dynamic images is performed by using the wavelet transform and PCA. And then, those are modeled by the HMM. Finally, we obtained better performance result from merging the recognition results for the static images and dynamic images.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• Science of Emotion and Sensibility
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• v.13 no.4
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• pp.635-646
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• 2010

This article focuses on Funology and a new classified application system based on concept of language and thought which are formed by body experience. It is defined by Funology Body as that. Funology Body is classifying and searching system which are consisted of a body, world (environment), and device tool. The body is sectioned by Brain, Eyes, Ears, Nose, Mouth, Hand, Torso, Feet, and Heart according as parts of the human body. This allows intuiting and experience searching as making classified system connected to the application relationship with concept of an each part of body. The Brain of the body is sub-classified by Book, Account, Business, Memory, Education, Search, and Aphorism to imply the application with thought. The Eyes take Video, Photography, and Broadcast for visibility. The Ears is categorized as Music, Instrument, Audio, and Radio for hearing. The Nose gets Perfume, Smell for olfactory sense. The Mouth is sectioned by Food, SNS, Chatting, Email, and Blog for eating and communication. The Hand sorts into Games, Kits, and Editing to handle, create, and play. The Torso is grouped by Health, Medical, Dance, Sport, Fashion, and Testyuorself related by protecting internal and meaning of the body core. The Feet is classified by Travel, Transportation, Map, and Outdoor for moving and concept of expanding the terrain. The Heart is consisted of Fear, Anger, Joy, Sadness, Acceptance, Disgust, Expectation, and Surprise for a human feeling. Beyond that, the World takes News, Time, Weather, Map, Fortune, and Shop, and Device tool gets Interface, Utilities. The Funology Body has a unique characteristic of giving intuitive and sensuous pleasure and reflection of users' attitude and taste for changing application flexibly.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.4
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• pp.205-215
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• 2014

This study analyzed 'The representation of death' as expressed in the fashion photography posted on Facebook. Currently, there is a growing interest in 'Well-dying'. Also contemporary art and fashion is a trend that expressed what about death more than life. And today, Facebook is one of the current worldwide as a powerful communication. Death representation in contemporary fashion photography, as expressed in its first characteristic is 'Vanitas', and the main material was a skull. The shape of a skeleton of the symbol of death. Vanitas of inner meaning is vain, a mortal life's futility and death for the paradoxical emotion. The second characteristic is 'Phantom of the ruins'. This is like the darkness of death, and the shape represented in that space. And the death representation is depressed, gloomy atmosphere, dead-man and warm-less. Inner meaning is curiosity about the ghosts and the decadence romantic about the afterlife. The last characteristic is 'Grotesque'. This characteristic is the destruction of the body, fear of sadistic, and inhuman shape. This is parable with death that pain and fear of death, dark fantasy, the appearance of a contradiction modern society and cut off humanity. Inner meaning is the dark fear of death and the anger of wrong of the present society. At present, we have to reproduce the death, and what we want is eventually no one can escape 'Attention to death'. In addition, by expressing the solidarity between death and life is to want to get a consolation for the anxiety and afraid reality.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.471-480
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• 2007

Negative emotions play an important function as to human's existence. In this research, we employed the audio-visual film clips to induce negative emotions and examined the classified responses in the autonomic nervous system(ANS) due to each negative emotion.30 adults(22.6 years $old{\pm}1.24$, 15 males and 15 females) took part in this experiment. Through the preliminary experiment, 2 minutes film's stimuli were selected as the emotion-induced stimuli. During the period when participants were viewing and listening to the selected movie, EDA and ECG were examined as soon as one stimulus was displayed, participants were tested by completing the psychological appraisals of their experienced emotion due to each emotional stimulus. With regard to the result of analyzing the psychological responses, each negative emotion appropriately and effectively induced its target emotion. While concerning the result of analyzing ANS responses, each negative emotion induced its respective activation in ANS. What is more, compared with other types of negative emotional stimuli, the scaring stimulus induced higher activation of the sympathetic nervour system(SNS) as to the indexes in EDh and ECG. This research made segmentation of ANS responses to each negative emotion, which has its significance.

• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.147-156
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• 2011

Basic emotions such as happiness, sadness, anger, fear, and disgust have been widely used to investigate emotion-specific autonomic nervous system activity in many studies. On the contrary, surprise emotion, Suggested also as one of the basic emotions suggested by Ekman et al. (1983), has been least investigated. The purpose of this study was to provide a description of cardiovascular responses on surprise stimulus using electrocardiograph (ECG) and photoplethysmograph (PPG). ECG and PPG were recorded from 76 undergraduate students, as they were exposed to a visuo-acoustic surprise stimulus. Heart rate (HR), standard deviation of R-R interval (SD-RR), root mean square of successive R-R interval difference (RMSSD-RR), respiratory sinus arrhythmia (RSA), finger blood volume pulse amplitude (FBVPA), and finger pulse transit time (FPTT) were calculated before and after the stimulus presentation. Results show significant increase in HR, SD-RR, and RMSSD-RR, decreased FBVPA, and shortened FPTT. Evidence suggests that surprise emotion can be characterized by vasoconstriction and accelerated heart rate, sympathetic activation, and increased heart rate variability, parasympathetic activation. These results can be useful in developing an emotion theory, or profiling surprise-specific physiological responses, as well as establishing the basis for emotion recognition system in human-computer interaction.

• Journal of Korea Game Society
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• v.14 no.6
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• pp.7-18
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• 2014

People generally think that negative effects of violent game contents are more serious in active playing behavior than passive watching video. This research examined the negative emotions which could arise out of playing violent games as fear, anxiety, hate, state-anger, hostility, and depression and performed an experiment methodology to assess those emotion effects between playing and watching violent game. The results of this research found that the watching group of violent video game showed a stronger internal negative feeling such as hate and depression, but the playing group of violent game had a deeper state-anger and hostility as external negative feeling than watching group. This research concludes that each media delivers different negative feelings, and there is little difference in the intensity of negative effects between playing and watching violent game.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.27-32
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• 2006

Verbal communication is the most commonly used mean of communication. A spoken word carries a lot of informations about speakers and their emotional states. In this paper we designed a model to recognize emotional states in a speech, a first phase of two phases in developing a toy machine that recognizes emotional states in a speech. We conducted an experiment to extract and analyse the emotional state of a speaker in relation with speech. To analyse the signal output we referred to three characteristics of sound as vector inputs and they are the followings: frequency, intensity, and period of tones. Also we made use of eight basic emotional parameters: surprise, anger, sadness, expectancy, acceptance, joy, hate, and fear which were portrayed by five selected students. In order to facilitate the differentiation of each spectrum features, we used the wavelet transform analysis. We applied ANFIS (Adaptive Neuro Fuzzy Inference System) in designing an emotion recognition model from a speech. In our findings, inference error was about 10%. The result of our experiment reveals that about 85% of the model applied is effective and reliable.