• 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.

• ETRI Journal
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• 제35권6호
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• Journal of Information Processing Systems
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• 제9권1호
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• pp.173-188
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• 2013

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

• 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.

• 한국산업정보학회논문지
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• 제12권3호
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• pp.60-68
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• 2007

본 논문에서는 자동으로 사용자의 얼굴 표정을 인식할 수 있는 시스템을 제안한다. 제안된 시스템은 휴리스틱 정보를 기반으로 설계된 트리 구조를 이용하여 행복, 역겨움, 놀람의 감정과 무표정을 인식한다. 카메라로부터 영상이 들어오면 먼저 얼굴 특징 검출기에서 피부색 모델과 연결성분 분석을 이용하여 얼굴 영역을 획득한다. 그 후에 신경망 기반의 텍스처 분류기를 사용하여 눈 영역과 비 눈 영역으로 구분한 뒤 눈의 중심 영역과 에지 정보를 기반으로 하여 눈, 눈썹, 입 등의 얼굴 특징을 찾는다. 검출된 얼굴 특징들은 얼굴 표정 인식기에 사용되며 얼굴 인식기는 이를 기반으로 한 decision tree를 이용하여 얼굴 감정을 인식한다. 제안된 방법의 성능을 평가하기 위해 MMI JAFFE, VAK DB에서 총 180장의 이미지를 사용하여 테스트하였고 약 93%의 정확도를 보였다.

• ETRI Journal
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• 제32권5호
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• pp.784-794
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• 2010

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

• ETRI Journal
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• 제37권6호
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• pp.1199-1210
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• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

• Asia pacific journal of information systems
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• 제27권1호
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• pp.38-53
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• 2017

As sensor technologies and image processing technologies make collecting information on users' behavior easy, many researchers have examined automatic emotion recognition based on facial expressions, body expressions, and tone of voice, among others. Specifically, many studies have used normal cameras in the multimodal case using facial and body expressions. Thus, previous studies used a limited number of information because normal cameras generally produce only two-dimensional images. In the present research, we propose an artificial neural network-based model using a high-definition webcam and Kinect to recognize users' emotions from facial and bodily expressions when watching a movie trailer. We validate the proposed model in a naturally occurring field environment rather than in an artificially controlled laboratory environment. The result of this research will be helpful in the wide use of emotion recognition models in advertisements, exhibitions, and interactive shows.

• 정보처리학회논문지B
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• 제12B권7호
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• pp.795-802
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• 2005

본 논문에서는 일곱 가지의 기본적인 감정 정보를 자동으로 파악하고 얼굴을 PDA 상에서 렌더링할 수 있는 얼굴 표정의 인식 및 합성 시스템을 제시한다. 얼굴 표정 인식을 위해서 먼저 카메라로부터 획득한 영상으로부터 얼굴 부분을 검출한다. 그리고 나서 기하학적 또는 조명으로 인한 보정을 위해 정규화 과정을 거친다. 얼굴 표정을 분류하기 위해서는 Gabor wavelets 방법을 enhanced Fisher 모델과 결합하여 사용할 때가 가장 좋은 결과를 보였다. 본 표정 분류에서는 일곱 가지 감정 가중치가 결과로 제시되고, 그러한 가중 정보는 모바일 네트웍을 통하여PDA 상으로 전송되어 얼굴 표정 애니메이션에 이용되어진다. 또한 본 논문에서는 고유한 얼굴 캐릭터를 가진 3차워 아바타를 생성하기 위하여 카툰 쉐이딩 기법을 채택하였다. 실험 결과 감정 곡선을 이용한 얼굴 표정 애니메이션은 선형 보간법 보다 감정 변화의 타이밍을 표현하는데 더 효과적인 것으로 나타났다.

• 한국HCI학회논문지
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• 제9권1호
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• pp.23-28
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• 2014

In this study we focus on the effect of the interaction between humans and reactive television when emotion recognition through facial expression mechanism is used. Most of today's user interfaces in electronic products are passive and are not properly fitted into users' needs. In terms of the user centered device, we propose that the emotion based reactive television is the most effective in interaction compared to other passive input products. We have developed and researched next generation cognitive TV models in user centered. In this paper we present a result of the experiment that had been taken with Fraunhofer IIS $SHORE^{TM}$ demo software version to measure emotion recognition. This new approach was based on the real time cognitive TV models and through this approach we studied the relationship between humans and cognitive TV. This study follows following steps: 1) Cognitive TV systems can be on automatic ON/OFF mode responding to motions of people 2) Cognitive TV can directly select channels as face changes (ex, Neutral Mode and Happy Mode, Sad Mode, Angry Mode) 3) Cognitive TV can detect emotion recognition from facial expression of people within the fixed time and then if Happy mode is detected the programs of TV would be shifted into funny or interesting shows and if Angry mode is detected it would be changed to moving or touching shows. In addition, we focus on improving the emotion recognition through facial expression. Furthermore, the improvement of cognition TV based on personal characteristics is needed for the different personality of users in human to computer interaction. In this manner, the study on how people feel and how cognitive TV responds accordingly, plus the effects of media as cognitive mechanism will be thoroughly discussed.