• 한국멀티미디어학회논문지
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• 제20권5호
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• pp.748-757
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• 2017

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.

• 한국심리학회지 : 문화 및 사회문제
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• 제20권4호
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• pp.347-364
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• 2014

본 연구는 얼굴자극의 검사단계 표정변화와 검사 지연시간, 그리고 배경변화가 얼굴재인에 미치는 효과를 검증하기 위해 수행되었다. 실험 1에서는 학습단계에서 부정 표정 얼굴을 학습하고 검사단계에서 동일한 얼굴의 부정 표정과 중성 표정얼굴에 대한 재인 검사가 실시되었다. 실험 2에서는 학습단계에서 부정 표정 얼굴을 학습하고 검사단계에서 부정 표정과 긍정 표정얼굴에 대한 재인 검사가 실시되었다. 실험 3에서는 학습단계에서 중성 표정 얼굴을 학습하고, 검사단계에서 부정 표정과 중성 표정 얼굴에 대한 재인 검사가 실시되었다. 세 실험 모두 참가자들은 즉시 검사와 지연 검사 조건에 할당되었고, 재인검사에서 목표 얼굴자극들은 배경이 일치 조건으로 또한 불일치 조건으로 제시되었다. 실험 1과 실험2 모두에서 부적 표정에 대한 재인율이 높았다. 실험 3에서 중성 표정에 대한 재인율이 높았다. 즉, 세 개실험 모두에서 표정 일치 효과가 나타났다. 학습단계에서 제시된 얼굴 표정의 정서와는 상관없이 검사단계에서 표정이 학습단계와 일치할 때 얼굴 재인율은 증가하였다. 또한 표정 변화에 따른 효과는 배경 변화에 따라 상이하게 나타났다. 본 연구 결과로 얼굴은 표정이 달라지면 기억하기 힘들며, 배경의 변화와 시간 지연에 따라 영향을 받는 다는 점을 확인하였다.

• 대한가정학회지
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• 제45권2호
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• pp.133-145
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• 2007

The purpose of this research was to identify the importance of emotional development in early childhood, in children ages three to five, by examining the relationship between the variables in the children such as gender, age, and temperament, as well as their mothers' emotionality, in relation to emotional development. The participants included a total of 72 children between three and five years of age. The major findings are as follow: First, there were significant differences in emotional expression and emotional recognition between the boys and the girls. Additionally, the emotional recognition of the children increased as age increased, and more positive strategies for emotional regulation were used with the increasing age of the children. Temperament characteristics did not have any relationship with emotional expression or emotional recognition, while the strategies for emotional regulation were related to the temperament characteristics. Second, the emotional expressivity of the mother was related to the emotional expression and recognition of the child, but wes not associated with strategies for emotional regulation. The emotional reactivity of the mother was related to a child's strategies for emotional regulation, but not to emotional expression or recognition. Third, emotional development of the children wes influenced by the individual child variables and emotionality of the mother.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.337-351
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• 2021

Automatically recognizing facial expressions in video sequences is a challenging task because there is little direct correlation between facial features and subjective emotions in video. To overcome the problem, a video facial expression recognition method using spatiotemporal recurrent neural network and feature fusion is proposed. Firstly, the video is preprocessed. Then, the double-layer cascade structure is used to detect a face in a video image. In addition, two deep convolutional neural networks are used to extract the time-domain and airspace facial features in the video. The spatial convolutional neural network is used to extract the spatial information features from each frame of the static expression images in the video. The temporal convolutional neural network is used to extract the dynamic information features from the optical flow information from multiple frames of expression images in the video. A multiplication fusion is performed with the spatiotemporal features learned by the two deep convolutional neural networks. Finally, the fused features are input to the support vector machine to realize the facial expression classification task. The experimental results on cNTERFACE, RML, and AFEW6.0 datasets show that the recognition rates obtained by the proposed method are as high as 88.67%, 70.32%, and 63.84%, respectively. Comparative experiments show that the proposed method obtains higher recognition accuracy than other recently reported methods.

• 한국통신학회논문지
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• 제39A권2호
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• pp.92-99
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• 2014

본 논문은 결정 트리(Decision tree) 구조를 기반으로 한 표정 인식 방법을 제안한다. ASM(Active Shape Model)과 LBP(Local Binary Pattern)를 통해, 표정 영상들의 국소 특징들을 추출한다. 국소 특징들로부터 표정들을 잘 분류할 수 있는 판별 특징(Discriminant feature)들을 추출하고, 그 판별 특징들은 모든 조합의 각 두 가지 표정들을 분류시킨다. 분류를 통해 얻어진 정인식의 합을 통해, 정인식 최대화 기반 국소 영역과 표정 조합을 결정한다. 이 가지 분류들을 종합하여, 결정 트리를 생성한다. 이 결정 트리 기반 표정 인식률은 약 84.7%로, 결정 트리를 고려하지 않은 방법보다, 더 좋은 인식 성능을 보였다.

• 한국컴퓨터정보학회논문지
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• 제27권9호
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• pp.41-47
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• 2022

본 논문에서는 자동표정인식을 위하여 얼굴 이미지 배열의 가운데 이미지를 예측하는 새롭고 간단한 자기주도학습 방법을 제안한다. 자동표정인식은 딥러닝 모델을 통해 높은 성능을 달성할 수 있으나 일반적으로 큰 비용과 시간이 투자된 대용량의 데이터 세트가 필요하고, 데이터 세트의 크기와 알고리즘의 성능이 비례한다. 제안하는 방법은 추가적인 데이터 세트 구축 없이 기존의 데이터 세트를 활용하여 자기주도학습을 통해 얼굴의 잠재적인 심층표현방법을 학습하고 학습된 파라미터를 전이시켜 자동표정인식의 성능을 향상한다. 제안한 방법은 CK+와 AFEW 8.0 두가지 데이터 세트에 대하여 높은 성능 향상을 보여주었고, 간단한 방법으로 큰 효과를 얻을 수 있음을 보여주었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.132-134
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• 2004

Emotion recognition between human and human is done compositely using various features that are face, voice, gesture and etc. Among them, it is a face that emotion expression is revealed the most definitely. Human expresses and recognizes a emotion using complex and various features of the face. This paper proposes hybrid feature extraction for emotions recognition from facial expression. Hybrid feature extraction imitates emotion recognition system of human by combination of geometrical feature based extraction and color distributed histogram. That is, it can robustly perform emotion recognition by extracting many features of facial expression.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 추계학술발표대회
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• pp.34-37
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• 2007

Face recognition is still a challenging problem in pattern recognition field which is affected by different factors such as facial expression, illumination, pose etc. The facial feature such as eyes, nose, and mouth constitute a complete face. Mouth feature of face is under the undesirable effect of facial expression as many factors contribute the low performance. We proposed a new approach for face recognition under facial expression applying two cascaded classifiers to improve recognition rate. All facial expression images are treated by general purpose classifier at first stage. All rejected images (applying threshold) are used for adaptation using GA for improvement in recognition rate. We apply Gabor Wavelet as a general classifier and Gabor wavelet with Genetic Algorithm for adaptation under expression variance to solve this issue. We have designed, implemented and demonstrated our proposed approach addressing this issue. FERET face image dataset have been chosen for training and testing and we have achieved a very good success.

• 반도체디스플레이기술학회지
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• 제12권3호
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• pp.53-57
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• 2013

Human emotions are expressed in various ways. It can be expressed through language, facial expression and gestures. In particular, the facial expression contains many information about human emotion. These vague human emotion appear not in single emotion, but in combination of various emotion. This paper proposes a emotional expression algorithm using Active Appearance Model(AAM) and Fuzz k- Nearest Neighbor which give facial expression in similar with vague human emotion. Applying Mahalanobis distance on the center class, determine inclusion level between center class and each class. Also following inclusion level, appear intensity of emotion. Our emotion recognition system can recognize a complex emotion using Fuzzy k-NN classifier.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1175-1186
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• 2017

Human emotion recognition is one of the promising applications in the era of artificial super intelligence. Thus far, facial expression traits are considered to be the most widely used information cues for realizing automated emotion recognition. This paper proposes a novel facial expression recognition (FER) method that works well for recognizing emotion from image sequences. To this end, we develop the so-called weighted soft voting classification (WSVC) algorithm. In the proposed WSVC, a number of classifiers are first constructed using different and multiple feature representations. In next, multiple classifiers are used for generating the recognition result (namely, soft voting) of each face image within a face sequence, yielding multiple soft voting outputs. Finally, these soft voting outputs are combined through using a weighted combination to decide the emotion class (e.g., anger) of a given face sequence. The weights for combination are effectively determined by measuring the quality of each face image, namely "peak expression intensity" and "frontal-pose degree". To test the proposed WSVC, CK+ FER database was used to perform extensive and comparative experimentations. The feasibility of our WSVC algorithm has been successfully demonstrated by comparing recently developed FER algorithms.