• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2000년도 춘계 학술대회 및 국제 감성공학 심포지움 논문집 Proceeding of the 2000 Spring Conference of KOSES and International Sensibility Ergonomics Symposium
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• pp.26-29
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• 2000

This study would review how important to define emotional intelligence could be in measuring individual differences in emotional experience. For this purpose, the definitions of emotion, built by psychological theorists, would first reviewed, in connection to the newly developed techniques in the area of emotional engineering. Comparing peripheral theories of emotion and cognitive appraisal theories, the multi-facets of emotional experience would be illustrated and it would discussed what these facets mean to predict emotional health of individuals.

• 반도체디스플레이기술학회지
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• 제16권4호
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• pp.79-85
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• 2017

Understanding of human emotion has a high importance in interaction between human and machine communications systems. The most expressive and valuable way to extract and recognize the human's emotion is by facial expression analysis. This paper presents and implements an automatic extraction and recognition scheme of facial expression and emotion through still image. This method has three main steps to recognize the facial emotion: (1) Detection of facial areas with skin-color method and feature maps, (2) Creation of the Bezier curve on eyemap and mouthmap, and (3) Classification and distinguish the emotion of characteristic with Hausdorff distance. To estimate the performance of the implemented system, we evaluate a success-ratio with emotional face image database, which is commonly used in the field of facial analysis. The experimental result shows average 76.1% of success to classify and distinguish the facial expression and emotion.

• 반도체디스플레이기술학회지
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• 제18권1호
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• pp.97-101
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• 2019

Understanding and classification of the human's emotion play an important tasks in interacting with human and machine communication systems. This paper proposes a novel emotion recognition method by extracting facial keypoints, which is able to understand and classify the human emotion, using active Appearance Model and the proposed classification model of the facial features. The existing appearance model scheme takes an expression of variations, which is calculated by the proposed classification model according to the change of human facial expression. The proposed method classifies four basic emotions (normal, happy, sad and angry). To evaluate the performance of the proposed method, we assess the ratio of success with common datasets, and we achieve the best 93% accuracy, average 82.2% in facial emotion recognition. The results show that the proposed method effectively performed well over the emotion recognition, compared to the existing schemes.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(A)
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• pp.255-258
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• 2011

An application of Gammatone auditory filterbank for emotion recognition from speech is described in this paper. Gammatone filterbank is a bank of Gammatone filters which are used as a preprocessing stage before applying feature extraction methods to get the most relevant features for emotion recognition from speech. In the feature extraction step, the energy value of output signal of each filter is computed and combined with other of all filters to produce a feature vector for the learning step. A feature vector is estimated in a short time period of input speech signal to take the advantage of dependence on time domain. Finally, in the learning step, Hidden Markov Model (HMM) is used to create a model for each emotion class and recognize a particular input emotional speech. In the experiment, feature extraction based on Gammatone filterbank (GTF) shows the better outcomes in comparison with features based on Mel-Frequency Cepstral Coefficient (MFCC) which is a well-known feature extraction for speech recognition as well as emotion recognition from speech.

• 제어로봇시스템학회논문지
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• 제19권6호
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• pp.513-519
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• 2013

The current vision-based approaches for emotion recognition, such as facial expression analysis, have many technical limitations in real circumstances, and are not suitable for applications that use them solely in practical environments. In this paper, we propose an approach for emotion recognition by combining extrinsic representations and intrinsic activities among the natural responses of humans which are given specific imuli for inducing emotional states. The intrinsic activities can be used to compensate the uncertainty of extrinsic representations of emotional states. This combination is done by using PRMs (Probabilistic Relational Models) which are extent version of bayesian networks and are learned by greedy-search algorithms and expectation-maximization algorithms. Previous research of facial expression-related extrinsic emotion features and physiological signal-based intrinsic emotion features are combined into the attributes of the PRMs in the emotion recognition domain. The maximum likelihood estimation with the given dependency structure and estimated parameter set is used to classify the label of the target emotional states.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.146-154
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• 2020

Negative emotion causes stress and lack of attention concentration. The classification of negative emotion is important to recognize risk factors. To classify emotion status, various methods such as questionnaires and interview are used and it could be changed by personal thinking. To solve the problem, we acquire multi modal bio-signals such as electrocardiogram (ECG), skin temperature (ST), galvanic skin response (GSR) and extract features. The neural network (NN), the deep neural network (DNN), and the deep belief network (DBN) is designed using the multi modal bio-signals to analyze emotion status. As a result, the DBN based on features extracted from ECG, ST and GSR shows the highest accuracy (93.8%). It is 5.7% higher than compared to the NN and 1.4% higher than compared to the DNN. It shows 12.2% higher accuracy than using only single bio-signal (GSR). The multi modal bio-signal acquisition and the deep learning classifier play an important role to classify emotion.

• 산업경영시스템학회지
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• 제35권2호
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• pp.64-70
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• 2012

This paper presents a statistical analysis method for the selection of electroencephalogram (EEG) electrode positions and spectral features to recognize emotion, where emotional valence and arousal are classified into three and two levels, respectively. Ten experiments for a subject were performed under three categorized IAPS (International Affective Picture System) pictures, i.e., high valence and high arousal, medium valence and low arousal, and low valence and high arousal. The electroencephalogram was recorded from 12 sites according to the international 10~20 system referenced to Cz. The statistical analysis approach using ANOVA with Tukey's HSD is employed to identify statistically significant EEG electrode positions and spectral features in the emotion recognition.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.282-287
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• 2006

This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an 'Interactive Feature Selection'. By performing an IFS, we were able to obtain three top features and apply them to the ERS. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

• 감성과학
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• 제14권1호
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• pp.157-164
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• 2011

감성과학은 현대 사회에서 점차 중요한 부분을 차지하고 있는 과학, 공학적 영역이다. 감성은 외부의 물리/화학적인 자극에 대한 인간 내부의 고차원적인 심리적 체험으로 기쁨, 슬픔, 쾌적, 불쾌 등에 대한 복합적인 감정이라 할 수 있다. 그러나 감성연구의 가장 큰 어려움은 측정의 문제이다. 기존 감성 측정은 자기보고, 인터뷰, 뇌파 및 자율 신경계 반응, 심장혈관 활동도 등에 국한되어 있고 여전히 객관적인 측정이라 할 수 없다. 따라서 우리는 혈액, 침, 땀 등의 체액을 이용해 실시간으로 인간의 감성을 정확하게 측정하는 Eomotion-on-a-chip (EOC)로 명명한 새로운 이름의 바이오칩 기술에 대해 제안한다. EOC는 감성을 측정하기 위한 바이오 마커와 신호를 얻기 위한 전극, 신호를 변환하기 위한 변환기, 그리고 측정의 결과를 보여주는 부분으로 구성된다. 최근 나노/마이크로 기술의 발달은 체액 내 감성 바이오 마커를 찾아내고 그것의 유무와 뇌과학 연구결과와 의 상관관계를 규명하고 미래에 피 한 방울로 인간의 심리상태를 정확히 파악 할 수 있는 초소형 감성진단칩을 개발하게 할 수 있다. 본 논문은 이제 막 연구가 시작되고 있는 미래 바이오칩기술의 하나인 EOC의 개념을 보고하는 리뷰논문이다.

• 한국화재소방학회논문지
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• 제32권2호
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• pp.92-101
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• 2018

본 연구는 외상사건에 반복적으로 노출되는 소방공무원들의 외상 후 스트레스가 자살에 미치는 영향을 살펴보고, 외상 후 스트레스와 자살생각과의 관계에서 인지적 정서조절의 조절효과를 검증하고자 하였다. 이를 위해 외상 후 스트레스, 한국판 자살생각, 인지적 정서조절을 측정하였다. 연구결과는 다음과 같다. 첫째, 외상 후 스트레스, 적응적 인지적 정서조절, 부적응적 인지적 정서조절은 상관관계가 있음이 확인되었다. 둘째, 외상 후 스트레스와 자살생각과의 관계에서 인지적 정서조절의 조절효과를 알아보기 위해 위계적 회귀분석을 실시한 결과, 적응적 인지적 정서조절은 낮은 외상 후 스트레스 집단에서 조절효과를 가졌고, 부적응적 인지적 정서조절은 높은 외상 후 스트레스 집단에서 조절효과를 갖는 것으로 확인되었다. 이러한 결과를 바탕으로 본 연구에서는 학문적, 임상적 시사점과 함께 후속 연구의 필요성에 대해 논의하였다.