• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.49-54
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• 2017

In this paper, we use face database to detect human emotion in real time. Although human emotions are defined globally, real emotional perception comes from the subjective thoughts of the judging person. Therefore, judging human emotions using computer image processing technology requires high technology. In order to recognize the emotion, basically the human face must be detected accurately and the emotion should be recognized based on the detected face. In this paper, based on the Cohn-Kanade Database, one of the face databases, faces are detected by combining the detected faces with the database.

• Science of Emotion and Sensibility
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• v.23 no.3
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• pp.63-78
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• 2020

It is crucial to process not only social cognitive information but also various emotional information for appropriate social interaction in everyday life. The processing of emotions embedded in social stimuli is affected by various context and external factors and the features of their own. Emotion discrimination tasks using point-light biological motion were conducted in this study to understand the factors influencing emotion processing and their effects. A target biological motion with angry or happy emotion was presented in the first task in red, green, white, or yellow color. A white angry, happy, or neutral "cue" biological motion was displayed simultaneously. Participants judged the emotion of the target relative to the cue stimulus by comparing the target with the cue. The second task used only emotionally neutral stimuli to find effect by the color itself. The results indicated an association between the specific color of the target and emotion. Red facilitated processing anger, whereas green appeared to facilitate happy emotion. The discrimination accuracy was high when the emotions of the cue and the target were identical in general, but the combination of red color and anger yielded different results compared with the rest of the conditions. Some illusory emotional responses associated with the target colors were observed in the second task. We could observe the effects of external factors in this study, affecting the emotional processing using biological motion rather than conventional face stimuli. Possible follow-up studies and clinical research were discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.178.5-178
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• 2001

We, Human beings, use both powers of reason and emotion simultaneously, which surely help us to obtain flexible adaptability against the dynamic environment. We assert that this principle can be applied into the general system. That is, it would be possible to improve the adaptability by covering a digital oriented information processing system with an analog oriented emotion layer. In this paper, we proposed a vertical slicing model with an emotion layer in It. And we showed that the emotion-based control allows us to improve the adaptability of a system at least under some conditions.

• Korean Journal of Biological Psychiatry
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• v.19 no.1
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• pp.9-16
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• 2012

This article aimed to describe typical event-related potentials (ERP) components of emotional processing in patients with anxiety disorder and highly anxious individuals. ERP components associated with emotional processing could be broadly divided into three components with short, middle and long, respectively. Many studies show that patients with anxiety disorders are characterized by different emotional bias to specific stimuli and more sensitive to emotional stimuli than normal individuals. In addition, these emotional biases were stronger and quicker in patients with anxiety disorder than normal individuals. Some studies reported that anxious people show abnormality at the initial stage (e.g. P1) of emotional processing. However, other studies reported the abnormality at the late stage (e.g. LPP) or wholeness of emotional processing in anxious individuals. We summarized the updated finding of possible ERP components of emotional processing in patients with anxiety disorder and highly anxious individuals. The significance and clinical implication were discussed.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.318-324
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• 2020

The present study was conducted to investigate the influences of emotion priming and the number of negation words on the task of sentential predicate reasoning in groups with or without anxiety symptoms. 3 types of primed emotions and 2 types of stimulus and 3 conditions of negation words were used as a within-subject variable. The subjects were instructed to make facial expressions that match the directions, and were asked to choose the correct answer from the given examples. Mixed repeated measured ANOVA analyses on reaction time first showed main effects for the variables of emotion, stimulus, number of negation words and anxiety level, and the interaction effects for the negation words x anxiety combination. These results are presumably suggested to reflect that externally intervening emotion works on language comprehension in a way that anxiety could delay task processing speed regardless of the emotion and stimulus type, meanwhile the number of negation words can slower language processing only in a anxiety group. Implications and limitations were discussed for the future work.

• Korean Journal of Cognitive Science
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• v.29 no.1
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• pp.39-59
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• 2018

To elucidate the effect of working memory (WM) load on negative facial emotion processing, we examined ERP components (P1 and N170) elicited by fearful and neutral expressions each of which was presented during 0-back (low-WM load) or 2-back (high-WM load) tasks. During N-back tasks, visual objects were presented one by one as targets and each of facial expressions was presented as a passively observed stimulus during intervals between targets. Behavioral results showed more accurate and fast responses at low-WM load condition compared to high-WM load condition. Analysis of mean amplitudes of P1 on the occipital region showed significant WM load effect (high-WM load > low-WM load) but showed nonsignificant facial emotion effect. Analysis of mean amplitudes of N170 on the posterior occipito-temporal region showed significant overall facial emotion effect (fearful > neutral), but, in detail, significant facial emotion effect was observed only at low-WM load condition on the left hemisphere, but was observed at high-WM load condition as well as low-WM load condition on the right hemisphere. To summarize, facial emotion effect observed by N170 amplitudes was modulated by WM load only on the left hemisphere. These results show that early emotional processing of negative facial expression could be eliminated or reduced by high load of WM on the left hemisphere, but could not be eliminated by high load on the right hemisphere, and suggest right hemispheric lateralization of negative facial emotion processing.

• Phonetics and Speech Sciences
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• v.2 no.1
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• pp.77-85
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• 2010

This paper proposes an efficient feature vector processing technique to guard the Speech Emotion Recognition (SER) system against a variety of noises. In the proposed approach, emotional feature vectors are extracted from speech processed by comb filtering. Then, these extracts are used in a robust model construction based on feature vector classification. We modify conventional comb filtering by using speech presence probability to minimize drawbacks due to incorrect pitch estimation under background noise conditions. The modified comb filtering can correctly enhance the harmonics, which is an important factor used in SER. Feature vector classification technique categorizes feature vectors into either discriminative vectors or non-discriminative vectors based on a log-likelihood criterion. This method can successfully select the discriminative vectors while preserving correct emotional characteristics. Thus, robust emotion models can be constructed by only using such discriminative vectors. On SER experiment using an emotional speech corpus contaminated by various noises, our approach exhibited superior performance to the baseline system.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1138-1156
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• 2021

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.146-148
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• 2011

홈 네트워크 분야는 현재 유무선 통신과 디지털 정보 기기를 기반으로 지능화 된 주거 공간을 제공하고자 하는 스마트 홈 연구가 활발하게 진행 중이다. 최근 사용자에게 더 편리하고 실용적인 스마트 홈 서비스를 어떻게 제공할 것인가에 대한 이슈가 대두되고 있다. 본 논문에서는 이를 위해 범용성, 유용성, 편의성을 고려한 서비스에 대해 연구하였다. 플랫폼 독립적인 웹서비스를 기반으로 홈 내 자원에 대한 기본 정보을 제공하고, 자원 사이의 관계 상황을 찾아 새로운 관계 정보를 제공한다. 이를 활용하여 사용자 편의적인 인터페이스를 가진 모바일 응용 프로그램을 개발하였다. 향후 장애 정보와 결합하여 홈 내 유지보수 서비스로의 확장을 기대한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.528-531
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• 2021

Nowadays recommendation systems are so ubiquitous, where our many decisions are being done by the means of them. We can see recommendation systems in all areas of our daily life. Therefore the research of this sphere is still so active. So far many research papers were published for clothing recommendations as well. In this paper, we propose the clothing-recommendation system according to user emotion and weather information. We used social media to analyze users' 6 basic emotions according to Paul Eckman theory and match the colour of clothing. Moreover, getting weather information using visualcrossing.com API to predict the kind of clothing. For sentiment analysis, we used Emotion Lexicon that was created by using Mechanical Turk. And matching the emotion and colour was done by applying Hayashi's Quantification Method III.