• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.562-567
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• 2009

As a key mechanism of the human emotion interaction, Facial Expression is a powerful tools in HRI(Human Robot Interface) such as Human Computer Interface. By using a facial expression, we can bring out various reaction correspond to emotional state of user in HCI(Human Computer Interaction). Also it can infer that suitable services to supply user from service agents such as intelligent robot. In this article, We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.191-196
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• 2005

Although the technology for emotion recognition is important one which was demanded in various fields, it still remains as the unsolved problems. Especially, there is growing demand for emotion recognition technology based on racial image. The facial image based emotion recognition system is complex system comprised of various technologies. Therefore, various techniques such that facial image analysis, feature vector extraction, pattern recognition technique, and etc, are needed in order to develop this system. In this paper, we propose new emotion recognition system based un previously studied facial image analysis technique. The proposed system recognizes the emotion by using the fuzzy classifier. The facial image database is built up and the performance of the proposed system is verified by using built database.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.535-549
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• 2024

Facial expressions (FEs) serve as fundamental components for human emotion assessment and human-computer interaction. Traditional convolutional neural networks tend to overlook valuable information during the FE feature extraction, resulting in suboptimal recognition rates. To address this problem, we propose a deep learning framework that incorporates hierarchical feature fusion, contextual data, and an attention mechanism for precise FE recognition. In our approach, we leveraged an enhanced VGGNet16 as the backbone network and introduced an improved group convolutional channel attention (GCCA) module in each block to emphasize the crucial expression features. A partial decoder was added at the end of the backbone network to facilitate the fusion of multilevel features for a comprehensive feature map. A reverse attention mechanism guides the model to refine details layer-by-layer while introducing contextual information and extracting richer expression features. To enhance feature distinguishability, we employed islanding loss in combination with softmax loss, creating a joint loss function. Using two open datasets, our experimental results demonstrated the effectiveness of our framework. Our framework achieved an average accuracy rate of 74.08% on the FER2013 dataset and 98.66% on the CK+ dataset, outperforming advanced methods in both recognition accuracy and stability.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.754-771
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• 2021

In the task of continuous dimension emotion recognition, the parts that highlight the emotional expression are not the same in each mode, and the influences of different modes on the emotional state is also different. Therefore, this paper studies the fusion of the two most important modes in emotional recognition (voice and visual expression), and proposes a two-mode dual-modal emotion recognition method combined with the attention mechanism of the improved AlexNet network. After a simple preprocessing of the audio signal and the video signal, respectively, the first step is to use the prior knowledge to realize the extraction of audio characteristics. Then, facial expression features are extracted by the improved AlexNet network. Finally, the multimodal attention mechanism is used to fuse facial expression features and audio features, and the improved loss function is used to optimize the modal missing problem, so as to improve the robustness of the model and the performance of emotion recognition. The experimental results show that the concordance coefficient of the proposed model in the two dimensions of arousal and valence (concordance correlation coefficient) were 0.729 and 0.718, respectively, which are superior to several comparative algorithms.

• Annual Conference of KIPS
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• 2023.05a
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• pp.510-512
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• 2023

In order to enhance a model's capability for detecting facial expressions, this research suggests a pipeline that makes use of the GradCAM component. The patching module and the pseudo-labeling module make up the pipeline. The patching component takes the original face image and divides it into four equal parts. These parts are then each input into a 2Dconvolutional layer to produce a feature vector. Each picture segment is assigned a weight token using GradCAM in the pseudo-labeling module, and this token is then merged with the feature vector using principal component analysis. A convolutional neural network based on transfer learning technique is then utilized to extract the deep features. This technique applied on a public dataset MMI and achieved a validation accuracy of 96.06% which is showing the effectiveness of our method.

• Science of Emotion and Sensibility
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• v.1 no.1
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• pp.33-40
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• 1998

본 논문은 얼굴인식 분야에 있어서 필수 과정인 얼굴 및 얼굴의 주요소인 눈과 입의 추출에 관한 방법을 제시한다. 얼굴 영역 추출은 복잡한 배경하에서 움직임 정보나 색상정보를 사용하지 않고 통계적인 모델에 기반한 일종의 형찬정합 방법을 사용하였다. 통계적인 모델은 입력된 얼굴 영상들의 Hotelling변환 과정에서 생성되는 고유 얼굴로, 복잡한 얼굴 영상을 몇 개의 주성분 갑으로 나타낼 수 있게 한다. 얼굴의 크기, 영상의 명암, 얼굴의 위치에 무관하게 얼굴을 추출하기 위해서, 단계적인 크기를 가지는 탐색 윈도우를 이용하여 영상을 검색하고 영상 강화 기법을 적용한 후, 영상을 고유얼굴 공간으로 투영하고 복원하는 과정을 통해 얼굴을 추출한다. 얼굴 요소의 추출은 각 요소별 특성을 고려한 엣지 추출과 이진화에 따른 프로젝션 히스토그램 분석에 의하여 눈과 입의 경계영역을 추출한다. 얼굴 영상에 관련된 윤곽선 추출에 관한 기존의 연구에서 주로 기하학적인 모양을 갖는 눈과 입의 경우에는 주로 가변 템플릿(Deformable Template)방법을 사용하여 특징을 추출하고, 비교적 다양한 모양을 갖는 눈썹, 얼굴 윤곽선 추출에는 스네이크(Snakes: Active Contour Model)를 이용하는 연구들이 이루어지고 있는데, 본 논문에서는 이러한 기존의 연구와는 달리 스네이크를 이용하여 적절한 파라미터의 선택과 에너지함수를 정의하여 눈과 입의 윤곽선 추출을 실험하였다. 복잡한 배경하에서 얼굴 영역의 추출, 추출된 얼굴 영역에서 눈과 입의 영역 추출 및 윤곽선 추출이 비교적 좋은 결과를 보이고 있다.