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

• 융합보안논문지
• /
• 제19권1호
• /
• pp.67-78
• /
• 2019

본 논문에서는 가변 크기 블록 기반의 새로운 얼굴 특징 표현 방법을 제안한다. 기존 외형 기반의 얼굴 표정 인식 방법들은 얼굴 특징을 표현하기 위해 얼굴 영상 전체를 균일한 블록으로 분할하는 uniform grid 방법을 사용하는데, 이는 다음 두가지 문제를 가지고 있다. 얼굴 이외의 배경이 포함될 수 있어 표정을 구분하는 데 방해 요소로 작용하고, 각 블록에 포함된 얼굴의 특징은 입력영상 내 얼굴의 위치, 크기 및 방위에 따라 달라질 수 있다. 본 논문에서는 이러한 문제를 해결하기 위해 유의미한 표정변화가 가장 잘 나타내는 블록의 크기와 위치를 결정하는 가변 크기 블록 방법을 제안한다. 이를 위해 얼굴의 특정점을 추출하여 표정인식에 기여도가 높은 얼굴부위에 대하여 블록 설정을 위한 기준점을 결정하고 AdaBoost 방법을 이용하여 각 얼굴부위에 대한 최적의 블록 크기를 결정하는 방법을 제시한다. 제안된 방법의 성능평가를 위해 LDTP를 이용하여 표정특징벡터를 생성하고 SVM 기반의 표정 인식 시스템을 구성하였다. 실험 결과 제안된 방법이 기존의 uniform grid 기반 방법보다 우수함을 확인하였다. 특히, 제안된 방법이 형태와 방위 등의 변화가 상대적으로 큰 MMI 데이터베이스에서 기존의 방법보다 상대적으로 우수한 성능을 보여줌으로써 입력 환경의 변화에 보다 효과적으로 적응할 수 있음을 확인하였다.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (2)
• /
• pp.688-690
• /
• 2004

In this paper, we propose a new method of removing glasses from human frontal facial images. We first detect the regions occluded by the glasses, and generate a natural looking facial image without glasses by recursive error compensation using PCA reconstruction. The resulting image has no trace of the glasses frame, nor of the reflection and shade caused by the glasses. The experimental results show that the proposed method provides an effective solution to the problem of glasses occlusion, and we believe that this method can also be used to enhance the performance of face recognition systems.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2011년도 추계학술발표대회
• /
• pp.351-354
• /
• 2011

This paper discusses on facial features extraction based on proposed skin color model. Different parts of face from input image are segmented based on skin color model. Moreover, this paper also discusses on concept to detect the eye and mouth position on face. A height and width ratio (${\delta}=1.1618$) based technique is also proposed to accurate detection of face region from the segmented image. Finally, we have cropped the desired part of the face. This exactly exacted face part is useful for face recognition and detection, facial feature analysis and expression analysis. Experimental results of propose method shows that the proposed method is robust and accurate.

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

• 한국지능시스템학회논문지
• /
• 제19권4호
• /
• pp.562-567
• /
• 2009

얼굴 표정은 사람의 감정을 전달하는 핵심 메커니즘으로 이를 적절하게 활용할 경우 Robotics의 HRI(Human Robot Interface)와 같은 Human Computer Interaction에서 큰 역할을 수행할 수 있다. 이는 HCI(Human Computing Interface)에서 사용자의 감정 상태에 대응되는 다양한 반응을 유도할 수 있으며, 이를 통해 사람의 감정을 통해 로봇과 같은 서비스 에이전트가 사용자에게 제공할 적절한 서비스를 추론할 수 있도록 하는 핵심요소가 된다. 본 논문에서는 얼굴표정에서의 감정표현을 인식하기 위한 방법으로 FACS(Facial Action Coding System)와 AAM(Active Appearance Model)을 이용한 특징 추출과 Bayesian Network 기반 표정 추론 기법이 융합된 얼굴표정 인식 시스템의 개발에 대한 내용을 제시한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권4호
• /
• pp.1486-1501
• /
• 2021

The removal of accessories from the face is one of the essential pre-processing stages in the field of face recognition. However, despite its importance, a robust solution has not yet been provided. This paper proposes a network and dataset construction methodology to remove only the glasses from facial images effectively. To obtain an image with the glasses removed from an image with glasses by the supervised learning method, a network that converts them and a set of paired data for training is required. To this end, we created a large number of synthetic images of glasses being worn using facial attribute transformation networks. We adopted the conditional GAN (cGAN) frameworks for training. The trained network converts the in-the-wild face image with glasses into an image without glasses and operates stably even in situations wherein the faces are of diverse races and ages and having different styles of glasses.

• 대한인간공학회지
• /
• 제30권1호
• /
• pp.195-202
• /
• 2011

Objective: The aim of this study is to investigate and review the previous researches about objective measuring fatigue caused by visual stimuli. Also, we analyze possibility of alternative visual fatigue measurement methods using facial expression recognition and gesture recognition. Background: In most previous researches, visual fatigue is commonly measured by survey or interview based subjective method. However, the subjective evaluation methods can be affected by individual feeling's variation or other kinds of stimuli. To solve these problems, signal and image processing based visual fatigue measurement methods have been widely researched. Method: To analyze the signal and image processing based methods, we categorized previous works into three groups such as bio-signal, brainwave, and eye image based methods. Also, the possibility of adopting facial expression or gesture recognition to measure visual fatigue is analyzed. Results: Bio-signal and brainwave based methods have problems because they can be degraded by not only visual stimuli but also the other kinds of external stimuli caused by other sense organs. In eye image based methods, using only single feature such as blink frequency or pupil size also has problem because the single feature can be easily degraded by other kinds of emotions. Conclusion: Multi-modal measurement method is required by fusing several features which are extracted from the bio-signal and image. Also, alternative method using facial expression or gesture recognition can be considered. Application: The objective visual fatigue measurement method can be applied into the fields of quantitative and comparative measurement of visual fatigue of next generation display devices in terms of human factor.

• 대한전자공학회논문지TE
• /
• 제37권3호
• /
• pp.104-113
• /
• 2000

본 연구에서는 웨이브렛 변환과 신경망 기반 얼굴 인식 알고리즘을 제안한다. 이 알고리즘은 일정한 조도 상태에서 두 개의 영상을 그레이 레벨로 취득하고 가우시안 필터를 이용하여 영상 내에 존재하는 잡음을 제 거한 후 배경영상과 얼굴이 포함된 입력영상의 차를 구하여 차영상에 대해 축소와 팽창과정을 통한 전처리 과정을 거치게 된다. 그리고 팽창 영상으로부터 마스크를 생성하여 마스크를 얼굴이 존재하는 원 영상에 투영하여 배경과 얼굴을 분할하고 분할된 얼굴영상의 에지를 조사하여 눈, 코, 입, 눈썹 그리고 뺨이 포함된 사 각 모양의 특징영역을 검출한다. 그리고 특징영역에 대해 이산 웨이브렛 변환을 수행하여 특징벡터를 추출하고 정규화한 후 신경망의 입력벡터로 하여 학습에 의한 인식을 수행한다. 시뮬레이션 결과 학습된 영상에 대해서는 100%의 인식률을 보였고 학습되지 않는 실험적 영상에 대해서도 92%의 인식률을 나타내었다.

• Journal of Acupuncture Research
• /
• 제18권1호
• /
• pp.1-13
• /
• 2001

The Background and Purpose : Most diagnostic method for the facial palsy were invasive and complex. And we don't know very well prognosis for the recovery of facial palsy in the first stage after the onset. But the Digital Infrared Thermal Image(DITI) isn't invasive and complex diagnostic method for the facial palsy. So we should study on the clinical prognostic diagnosis of Bell's palsy among facial palsy with the DITI. Objective and Methods : This study researched into the clinical statistics for 89 case who are in Bell's palsy, and they are treated with oriental medical care at the Woosuk university during 2 years form November 1998 to October 2000. All objectives have the Grade 6(Zero state) of Bell's palsy in first week after the onset. It takes a patient's facial temperature after the onset. Group A is taken from 1 day to 4 days after the onset. Group B is taken from 5 day to 8 days after the onset. And group C is taken from 9 day to 12 days after the onset. Results and Conclusions : The Digital Infrared thermal image technique showed the more high temperature, the more rapid cure and short treatment period on TE23, B2, S3, S6 in abnormal site of Bell's palsy. But it showed the more low temperature, the more rapid cure and short treatment period on TE17 of abnormal site of Bell's palsy. As a conclusion, we could think that the prognostic diagnosis of Bell's palsy closely related with the thermal difference normal and abnormal site of Bell's palsy that were took picture after the onset.