• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.717-724
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• 2020

Due to the important role played by emotion in human interaction, affective computing is dedicated in trying to understand and regulate emotion through human-aware artificial intelligence. By understanding, emotion mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction will be better managed as they are all associated with emotion. Various studies for emotion recognition have been conducted to solve these problems. In applying machine learning for the emotion recognition, the efforts to reduce the complexity of the algorithm and improve the accuracy are required. In this paper, we investigate emotion Electroencephalogram (EEG) feature reduction and classification using Stack AutoEncoder (SAE) and Long-Short-Term-Memory/Recurrent Neural Networks (LSTM/RNN) classification respectively. The proposed method reduced the complexity of the model and significantly enhance the performance of the classifiers.

• Science of Emotion and Sensibility
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• v.26 no.1
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• pp.33-42
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• 2023

Previous emotion studies employing facial expressions have focused on the differences between age groups for each of the emotion categories. Instead, Kim (2021) has compared representations of facial expressions in the lower-dimensional emotion space. However, he reported descriptive comparisons without statistical significance testing. This research used representational similarity analysis (Kriegeskorte et al., 2008) to directly compare empirical datasets from young, middle-aged, and old groups and conceptual models. In addition, individual differences multidimensional scaling (Carroll & Chang, 1970) was conducted to explore individual weights on the emotional dimensions for each age group. The results revealed that the old group was the least similar to the other age groups in the empirical datasets and the valence model. In addition, the arousal dimension was the least weighted for the old group compared to the other groups. This study directly tested the differences between the three age groups in terms of empirical datasets, conceptual models, and weights on the emotion dimensions.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1155-1163
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• 2009

This paper proposes an automatic facial expression generation system of vector graphic character using gaussian process model. Proposed method extracts the main feature vectors from twenty-six facial data of character redefined based on Russell's internal emotion state. Also by using new gaussian process model, SGPLVM, we find low-dimensional feature data from extracted high-dimensional feature vectors, and learn probability distribution function (PDF). All parameters of PDF are estimated by maximization the likelihood of learned expression data, and these are used to select wanted facial expressions on two-dimensional space in real time. As a result of simulation, we confirm that proposed facial expression generation tool is working in the small facial expression datasets and can generate various facial expressions without prior knowledge about relation between facial expression and emotion.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.248-252
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• 2000

Form-ratio means the ratio of height/Width/Depth in 3-dimensions. The golden ratio or golden section is included as one of the form-ratio. We conducted two kinds of kansei experiments of cubic model and refrigerator varied from 1:1:1 to 1:1:3.66 on the scale of x:y:z. The subjects evaluate the form-ratios of 3-dimensional cubes and virtual products with SD-scale Kansei words(feelings and images). We applied the factor analysis to identify semantic space in cube model and virtual products. Finally, we compared with kansei structure of cube model and virtual product.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.351-360
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• 2018

Human emotion recognition is a research topic that is receiving continuous attention in computer vision and artificial intelligence domains. This paper proposes a method for classifying human emotions through multiple neural networks based on multi-modal signals which consist of image, landmark, and audio in a wild environment. The proposed method has the following features. First, the learning performance of the image-based network is greatly improved by employing both multi-task learning and semi-supervised learning using the spatio-temporal characteristic of videos. Second, a model for converting 1-dimensional (1D) landmark information of face into two-dimensional (2D) images, is newly proposed, and a CNN-LSTM network based on the model is proposed for better emotion recognition. Third, based on an observation that audio signals are often very effective for specific emotions, we propose an audio deep learning mechanism robust to the specific emotions. Finally, so-called emotion adaptive fusion is applied to enable synergy of multiple networks. The proposed network improves emotion classification performance by appropriately integrating existing supervised learning and semi-supervised learning networks. In the fifth attempt on the given test set in the EmotiW2017 challenge, the proposed method achieved a classification accuracy of 57.12%.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.133-137
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• 2000

Form-ratio means the ratio of Height/Width/Depth in 3-dimensions. The golden ratio or golden section is included as one of the form-ratio. Kansei Engineering System has some basic design databases. Form-ratio and color are basic design elements and they are very important for designing various products in viewpoint of Kansei Engineering. The subjects evaluate the form-ratios of 3-dimensional cubes and virtual products (refrigerator) with SD-scale kansei words(feelings and images). The golden ration was evaluated as "not beautiful" in refrigerator. We compared with the kansei of cube model and virtual product, and obtained databases of the relationship between the form-ratio and kansei.

• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.637-644
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• 2008

Human emotion has been tried to be recognized by physiological measurements in developing emotion machine enabling to understand and react to user's emotion. This study is to find the time-dependent physiological measurements and their variation characteristics for discriminating emotions according to dimensional emotion model. Ten university students were asked to watch sixteen prepared images to evoke different emotions. Their subjective emotions and autonomic nervous responses such as ECG (electrocardiogram), PPG (photoplethysmogram), GSR (Galvanic skin response), RSP (respiration), and SKT(skin temperature) were measured during experiment. And these responses were analyzed into HR(Heart Rate), Respiration Rate, GSR amplitude average, SKT amplitude average, PPG amplitude, and PTT(Pulse Transition Time). TDPs(Time dependent parameters) defined as the delay, the activation, the half recovery and the full recovery of respective physiological signal in this study have been determined and statistically compared between variations from different emotions. The significant tendencies in TDP were shown between emotions. Therefore, TDP may provide useful measurements with emotion recognition.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.51-56
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• 2004

Arousal level has been well defined by autonomic responses. However, entire emotion including both valence and arousal level is often questioned to be completely described by only autonomic responses. This study is to find the autonomic physiological parameters which were used emotion evaluation, 15 undergraduate students were asked to watch eight video clips from diverse movies and comedy shows for experiencing emotions. The subjectively experienced emotion were grouped by three factors. Two dimensional emotion model having the pleasant-unpleasant and arousal-non arousal factors were mapped with three physiological responses(GSR, PPG, SKT). The results may suggest that PPG and GSR may be used as arousal index while SKT may pleasant index. And the complex relation of physiological responses to emotional experiences are discussed.

• Korean Journal of Cognitive Science
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• v.14 no.4
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• pp.33-42
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• 2003

This paper presents a new system for facial expression recognition based in dimension model of internal states. The information of facial expression are extracted to the three steps. In the first step, Gabor wavelet representation extracts the edges of face components. In the second step, sparse features of facial expressions are extracted using fuzzy C-means(FCM) clustering algorithm on neutral faces, and in the third step, are extracted using the Dynamic Model(DM) on the expression images. Finally, we show the recognition of facial expression based on the dimension model of internal states using a multi-layer perceptron. The two dimensional structure of emotion shows that it is possible to recognize not only facial expressions related to basic emotions but also expressions of various emotion.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.213-215
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• 2007

This paper introduces an emotional behavior decision model for intelligent service robots. An emotional model should make different behavior decisions according to the purpose of the robots. We propose an emotional behavior decision model which can change the character of emotional model and make different behavior decisions although the situation and environment remain the same. We defined each emotional element such as reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics by state dynamic equations. The proposed system model is a linear system. If you want to add one external stimulus or behavior, you need to add just one dimensional vector to the matrix of external stimulus or behavior dynamics. The case of removing is same. The change of reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics also follows the same procedure. We implemented the proposed emotional behavior decision model and verified its performance.