• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.125-139
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• 2023

Facial expression recognition, a topical problem in the field of computer vision and pattern recognition, is a direct means of recognizing human emotions and behaviors. This paper first summarizes the datasets commonly used for expression recognition and their associated characteristics and presents traditional machine learning algorithms and their benefits and drawbacks from three key techniques of face expression; image pre-processing, feature extraction, and expression classification. Deep learning-oriented expression recognition methods and various algorithmic framework performances are also analyzed and compared. Finally, the current barriers to facial expression recognition and potential developments are highlighted.

• 제어로봇시스템학회논문지
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• 제20권5호
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• pp.520-525
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• 2014

Generally, feature area detection methods are widely used for face expression recognition by detecting the feature areas of human eyes, eyebrows and mouth. In this paper, we proposed a face expression recognition method using the histograms of the face, eyes and mouth for many applications including robot technology. The experimental results show that the proposed method has a new type of face expression recognition capability compared to conventional methods.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• 제어로봇시스템학회논문지
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• 제12권2호
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• pp.130-137
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• 2006

In this paper, we propose a method of selecting new types of rectangle features that are suitable for facial expression recognition. The basic concept in this paper is similar to Viola's approach, which is used for face detection. Instead of previous Haar-like features we choose rectangle features for facial expression recognition among all possible rectangle types in a 3${\times}$3 matrix form using the AdaBoost algorithm. The facial expression recognition system constituted with the proposed rectangle features is also compared to that with previous rectangle features with regard to its capacity. The simulation and experimental results show that the proposed approach has better performance in facial expression recognition.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.881-886
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• 2012

The paper is facial expression recognition based on the inclined face geometrical information. In facial expression recognition, mouth has a key role in expressing emotions, in this paper the features is mainly based on the shapes of mouth, followed by eyes and eyebrows. This paper makes its efforts to disperse every feature values via the weighting function and proposes method of expression classification with excellent classification effects; the final recognition model has been constructed.

• 정보처리학회논문지B
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• 제16B권5호
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• pp.395-402
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• 2009

본 연구는 표정 인식률을 개선하기 위한, 강도가 다른 표정을 인식하는 새로운 표정 인식 방법을 제안한다. 사람마다 다르게 나타나는 표정과 표정마다 다른 강도는 표정 인식률 저하에 지대한 영향을 미친다. 하지만 얼굴 표정의 다양한 강도를 처리하는 방법은 많이 제시되지 않고 있다. 본 연구에서는 표정 템플릿과 표정 강도 분포모델을 이용하여 다양한 얼굴 표정 강도를 인식하는 방법을 제시한다. 표정 템플릿과 표정강도 분포모델은 얼굴의 특징 부위에 표시한 관심 점과 얼굴 특징 부위간의 움직임이 다른 표정과 강도에 따라 어떻게 달라지는지 설명하여 표정 인식률 개선에 기여한다. 제안 방법은 정지 이미지뿐만 아니라 비디오시퀀스에서도 빠른 측정 과정을 통해 다양한 강도의 표정을 인식할 수 있는 장점이 있다. 실험 결과, 제안 연구가 특히 약한 강도의 표정에 대해 타 방법보다 높은 인식 결과를 보여 제안 방법이 다양한 강도의 표정 인식에 강건함을 알 수 있다.

• 한국산업정보학회논문지
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• 제7권3호
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• pp.23-31
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• 2002

본 논문에서는 화상자료의 특성인 이웃 화소간의 종속성을 표현하는데 적합한 깁스분포를 바탕으로 얼굴 표정을 인식을 위한 특징벡터를 추출하는 새로운 방법을 제안하였다. 추출된 특징벡터는 얼굴 이미지의 크기, 위치, 회전에 대하여 불변한 특성을 갖는다. 얼굴 표정을 인식하기 위한 알고리즘은 특징벡터 추출하는 과정과 패턴을 인식하는 두 과정으로 나뉘어진다. 특징벡터는 얼굴 화상에 대하여 추정된 깁스분포를 바탕으로 수정된 2-D 조건부 모멘트로 구성된다. 얼굴 표정인식 과정에서는 패턴인식에 널리 사용되는 이산형 HMM를 사용한다. 제안된 방법에 대한 성능평가를 위하여 4가지의 얼굴 표정 인식 실험을 Workstation에서 실험한 결과, 제안된 얼굴 표정 인식 방법이 95% 이상의 성능을 보여주었다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권3호
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• pp.135-138
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• 2014

본 논문에서는 2D-DCT와 EHMM 알고리즘을 이용하여 과적합에 강인한 얼굴 표정인식 방법을 고안하였다. 특히, 본 논문에서는 2D-DCT 특징추출을 위한 윈도우 크기를 크게 설정하여 EHMM의 관측벡터를 추출함으로써, 표정인식 성능 향상을 도모하였다. 제안 방법의 성능평가는 공인 CK 데이터베이스와 JAFFE 데이터베이스를 이용하여 수행되었고, 실험 결과로부터 특징추출 윈도우의 크기가 커질수록 표정 인식률이 향상됨을 확인하였다. 또한, CK 데이터베이스를 이용하여 표정 모델을 생성하고 JAFFE 데이터베이스 전체 샘플을 테스트한 결과, 제안 방법은 87.79%의 높은 인식률을 보였으며, 기존의 히스토그램 특징 기반의 표정인식 접근법보다 46.01~50.05%의 향상된 인식률을 보였다.

• International Journal of Advanced Culture Technology
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• 제6권1호
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• pp.60-67
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• 2018

In this paper, the pattern of extracting the same expression is proposed by using the Android intelligent device to identify the facial expression. The understanding and expression of expression are very important to human computer interaction, and the technology to identify human expressions is very popular. Instead of searching for the symbols that users often use, you can identify facial expressions with a camera, which is a useful technique that can be used now. This thesis puts forward the technology of the third data is available on the website of the set, use the content to improve the infrastructure of the facial expression recognition accuracy, to improve the synthesis of neural network algorithm, making the facial expression recognition model, the user's facial expressions and similar expressions, reached 66%. It doesn't need to search for symbols. If you use the camera to recognize the expression, it will appear symbols immediately. So, this service is the symbols used when people send messages to others, and it can feel a lot of convenience. In countless symbols, there is no need to find symbols, which is an increasing trend in deep learning. So, we need to use more suitable algorithm for expression recognition, and then improve accuracy.

• 한국인공지능학회지
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• 제5권2호
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• pp.43-53
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• 2017

In this paper, the pattern of extracting the same expression is proposed by using the Android intelligent device to identify the facial expression. The understanding and expression of expression are very important to human computer interaction, and the technology to identify human expressions is very popular. Instead of searching for the emoticons that users often use, you can identify facial expressions with acamera, which is a useful technique that can be used now. This thesis puts forward the technology of the third data is available on the website of the set, use the content to improve the infrastructure of the facial expression recognition accuracy, in order to improve the synthesis of neural network algorithm, making the facial expression recognition model, the user's facial expressions and similar e xpressions, reached 66%.It doesn't need to search for emoticons. If you use the camera to recognize the expression, itwill appear emoticons immediately. So this service is the emoticons used when people send messages to others, and it can feel a lot of convenience. In countless emoticons, there is no need to find emoticons, which is an increasing trend in deep learning. So we need to use more suitable algorithm for expression recognition, and then improve accuracy.