• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.2
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• pp.92-99
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• 2014

This paper proposes the method of facial expression recognition based on decision tree structure. In the image of facial expression, ASM(Active Shape Model) and LBP(Local Binary Pattern) make the local features of a facial expressions extracted. The discriminant features gotten from local features make the two facial expressions of all combination classified. Through the sum of true related to classification, the combination of facial expression and local region are decided. The integration of branch classifications generates decision tree. The facial expression recognition based on decision tree shows better recognition performance than the method which doesn't use that.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.759-765
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• 2006

In this paper, it is proposed an effective face expression recognition LDA mixed mode using a triangularity membership fuzzy function and wavelet basis. The proposal algorithm gets performs the optimal image, fuzzy wavelet algorithm and Expression recognition is consisted of face characteristic detection step and face Expression recognition step. This paper could applied to the PCA and LDA in using some simple strategies and also compares and analyzes the performance of the LDA mixed model which is combined and the facial expression recognition based on PCA and LDA. The LDA mixed model is represented by the PCA and the LDA approaches. And then we calculate the distance of vectors dPCA, dLDA from all fates in the database. Last, the two vectors are combined according to a given combination rule and the final decision is made by NNPC. In a result, we could showed the superior the LDA mixed model can be than the conventional algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.1-7
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• 2006

In this paper, adaptive Facial Emotional Recognition system based on Gabor Wavelet Neural Network, considering six feature Points in face image to extract specific features of facial expression, is proposed. Levenberg-Marquardt-based training methodology is used to formulate initial network, including feature extraction stage. Therefore, heuristics in determining feature extraction process can be excluded. Moreover, to make an adaptive network for new user, Q-learning which has enhanced reward function and unsupervised fuzzy neural network model are used. Q-learning enables the system to ge optimal Gabor filters' sets which are capable of obtaining separable features, and Fuzzy Neural Network enables it to adapt to the user's change. Therefore, proposed system has a good on-line adaptation capability, meaning that it can trace the change of user's face continuously.

• Convergence Security Journal
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• v.19 no.1
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• pp.67-78
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• 2019

Most existing facial expression recognition methods use a uniform grid method that divides the entire facial image into uniform blocks when describing facial features. The problem of this method may include non-face backgrounds, which interferes with discrimination of facial expressions, and the feature of a face included in each block may vary depending on the position, size, and orientation of the face in the input image. In this paper, we propose a variable-size block method which determines the size and position of a block that best represents meaningful facial expression change. As a part of the effort, we propose the way to determine the optimal number, position and size of each block based on the facial feature points. For the evaluation of the proposed method, we generate the facial feature vectors using LDTP and construct a facial expression recognition system based on SVM. Experimental results show that the proposed method is superior to conventional uniform grid based method. Especially, it shows that the proposed method can adapt to the change of the input environment more effectively by showing relatively better performance than exiting methods in the images with large shape and orientation changes.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.63-68
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• 2018

Interests in face recognition area have been increasing due to diverse emerging applications. Face recognition algorithm from a two-dimensional source could be challenging in dealing with some circumstances such as face orientation, illuminance degree, face details such as with/without glasses and various expressions, like, smiling or crying. Hopfield Network capabilities have been used specially within the areas of recalling patterns, generalizations, familiarity recognitions and error corrections. Based on those abilities, a specific experimentation is conducted in this paper to apply the Redundant Parallel Hopfield Network on a face recognition problem. This new design has been experimentally confirmed and tested to be robust in any kind of practical situations.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.431-434
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• 2007

얼굴 인식은 생체인식 기술 중 비 강압식이라는 장점으로 인해 각광받고 있는 분야이다. 그러나 얼굴인식은 조명, 표정에 의해 인식 성능이 저하되는 단점이 있다. 그 중 얼굴표정에 많은 영향을 받으며, 잡음이 많은 부분이 입술부분이다. 입술모양의 변화에 따라 가보벡터 추출에 잡음이 포함되기 때문에, 얼굴 인식 성능이 저하되는 현상이 발생됨을 실험을 통해 알 수 있었다. 따라서 본 논문에서는 입술모양의 변화에 따른 잡음을 줄이기 위해 입술영역에 최적화된 말스버그 가보 웨이브렛 커널(Malsburg Gabor Wavelet Kerne)을 제안한다. 각 입술 특징점에 말스 버그 가보 웨이브렛을 적용하여, 추출된 가보벡터를 통계적으로 분석함으로써 잡음을 확인 할 수 있었으며, 잡음을 최소화하기 위해 입술 영역에 적응적인 말스버그 가보 웨이브렛 커널 을 제안하였다. 실험에 사용한 이미지는 1196 FERET Gellery 이미지를 사용하였으며, 얼굴 인식 성능이 향상됨을 알 수 있었다.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.160-163
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• 2006

If an instruction system understood the interest and activeness of a learner in real time, it could provide some interesting factors when a learner is tired of learning. It could work as an adaptive tutoring system to help a learner to understand something difficult to understand. Currently the area of the facial expression recognition mainly deals with the facial expression of adults focusing on anger, hatred, fear, sadness, surprising and gladness. These daily facial expressions couldn't be one of expressions of a learner in e-Learning. They should first study the facial expressions of a learner in e-Learning to recognize the feeling of a learner. Collecting as many expression pictures as possible, they should study the meaning of each expression. This study, as a prior research, analyzes the feelings of learners and facial expressions of learners in e-Learning in relation to the feelings to establish the facial expressions database.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.165-171
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• 2017

It is difficult to recognize Human's facial expression in the real-world. For these reason, when database and test data have similar condition, we can accomplish high accuracy. Solving these problem, we need to many facial expression data. In this paper, we propose the algorithm for gathering many facial expression data within various environment and gaining high accuracy quickly. This algorithm is training initial model with the ASSL (Active Semi-Supervised Learning) using deep learning network, thereafter gathering unlabeled facial expression data and repeating this process. Through using the ASSL, we gain proper data and high accuracy with less labor force.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.903-906
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• 2004

본 논문에서는 얼굴 표정 인식 시스템을 위한 얼굴 영역 추출 알고리즘을 제안한다. 이는 입력 영상으로부터 얼굴 후보 영역을 추출하고, 추출된 얼굴 후보 영역에서 눈의 위치를 정확히 추출한다. 그리고 추출된 눈 영역들의 정보와 타원 방정식을 이용하여 최종 얼굴 영역을 추출한다. 얼굴 후보 영역은 HSI 칼라 좌표계에 기반한 적응적 피부색 구간 범위를 설정하여 추출하였다. 추출된 얼굴 후보 영역에서의 눈 영역 추출을 위해 밝기 정보를 이용하여 먼저 눈의 후보 화소들을 추출하고, 레이블링 과정을 통하여 영역별로 그룹화하였다. 각 후보 영역들의 화소 수, 가로세로비 및 위치 정보를 고려하여 최종 눈 영역을 추출하였다. 추출된 두 눈 영역에서 무게중심을 구하고 이를 이용하여 장축과 단축을 설정하여 타원방정식을 이용 최종 얼굴 영역을 추출하였다. 제안된 알고리즘은 조명 변화, 다양한 배경들을 가지는 얼굴 영상에서도 정확히 얼굴 영역을 추출할 수 있었다.

• Science of Emotion and Sensibility
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• v.17 no.1
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• pp.53-62
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• 2014

Despite the huge potential of the practical application of emotion recognition technologies, the enhancement of the technologies still remains a challenge mainly due to the difficulty of recognizing emotion. Although not perfect, human emotions can be recognized through human images and sounds. Emotion recognition technologies have been researched by extensive studies that include image-based recognition studies, sound-based studies, and both image and sound-based studies. Studies on emotion recognition through facial expression detection are especially effective as emotions are primarily expressed in human face. However, differences in user environment and their familiarity with the technologies may cause significant disparities and errors. In order to enhance the accuracy of real-time emotion recognition, it is crucial to note a mechanism of understanding and analyzing users' personality traits that contribute to the improvement of emotion recognition. This study focuses on analyzing users' personality traits and its application in the emotion recognition system to reduce errors in emotion recognition through facial expression detection and improve the accuracy of the results. In particular, the study offers a practical solution to users with subtle facial expressions or low degree of emotion expression by providing an enhanced emotion recognition function.