• Journal of Digital Contents Society
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• v.7 no.4
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• pp.235-243
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• 2006

This paper presents the development of certain efficient method for expressing the emotion of an avatar based on the facial expression recognition. This new method is not changing a facial expression of the avatar manually. It can be changing a real time facial expression of the avatar based on recognition of a facial pattern which can be captured by a web cam. It provides a tool for recognizing some part of images captured by the web cam. Because of using the model-based approach, this tool recognizes the images faster than other approaches such as the template-based or the network-based. It is extracting the shape of user's lip after detecting the information of eyes by using the model-based approach. By using changes of lip's patterns, we define 6 patterns of avatar's facial expression by using 13 standard lip's patterns. Avatar changes a facial expression fast by using the pre-defined avatar with corresponding expression.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1901-1902
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• 2008

얼굴 인식은 얼굴의 특징적인 패턴을 이용하지만, 이러한 패턴은 표정, 포즈, 조명의 변화에 민감하여 인식에 어려움이 있다. 본 논문은 표정 변화에 강인한 인식 모델을 개발하기 위해 Cohn-Kanade 표정 데이터베이스와 AAM을 이용하여 다양한 데이터를 추출하였고, 추출된 데이터를 다선형 분석을 이용하여 분석하였다. 이를 적용한 인식 실험에서 PCA보다 표정에 좀 더 강인한 인식 성능을 나타내었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.167-170
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• 2006

본 논문은 선형분류기인 LDA 융합모델과 최소거리패턴분류법을 이용한 얼굴표정인식 알고리즘 연구에 관한 것이다. 제안된 알고리즘은 얼굴 표정을 인식하기 위해 두 단계의 특징 추출과정과 인식단계를 거치게 된다. 먼저 특징추출 단계에서는 얼굴 표정이 담긴 영상을 PCA를 이용해 고차원에서 저차원의 공간으로 변환한 후, LDA 이용해 특징벡터를 클래스 별로 나누어 분류한다. 다음 단계로 LDA융합모델을 통해 계산된 특징벡터에 최소거리패턴분류법을 적용함으로서 얼굴 표정을 인식한다. 제안된 알고리즘은 6가지 기본 감정(기쁨, 화남, 놀람, 공포, 슬픔, 혐오)으로 구성된 데이터베이스를 이용해 실험한 결과, 기존알고리즘에 비해 향상된 인식률과 특정 표정에 관계없이 고른 인식률을 보임을 확인하였다.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.570-579
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• 2005

Facial expression recognition technology that has potentialities for applying various fields is appling on the man-machine interface development, human identification test, and restoration of facial expression by virtual model etc. Using sequential facial images, this study proposes a simpler method for detecting human facial expressions such as happiness, anger, surprise, and sadness. Moreover the proposed method can detect the facial expressions in the conditions of the sequential facial images which is not rigid motion. We identify the determinant face and elements of facial expressions and then estimates the feature regions of the elements by using information about color, size, and position. In the next step, the direction patterns of feature regions of each element are determined by using optical flows estimated gradient methods. Using the direction model proposed by this study, we match each direction patterns. The method identifies a facial expression based on the least minimum score of combination values between direction model and pattern matching for presenting each facial expression. In the experiments, this study verifies the validity of the Proposed methods.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.284-291
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• 2005

I propose a framework for detecting, recognizing and classifying facial features based on learned expression patterns. The framework recognizes facial expressions by using PCA and expression HMM(EHMM) which is Hidden Markov Model (HMM) approach to represent the spatial information and the temporal dynamics of the time varying visual expression patterns. Because the low level spatial feature extraction is fused with the temporal analysis, a unified spatio-temporal approach of HMM to common detection, tracking and classification problems is effective. The proposed recognition framework is accomplished by applying posterior probability between current visual observations and previous visual evidences. Consequently, the framework shows accurate and robust results of recognition on as well simple expressions as basic 6 facial feature patterns. The method allows us to perform a set of important tasks such as facial-expression recognition, HCI and key-frame extraction.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.23-31
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• 2002

In this paper we propose a new feature vector for recognition of the facial expression based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are invariant under translation rotation, and scale of an facial expression imege. The Algorithm for recognition of a facial expression contains two parts： the extraction of feature vector and the recognition process. The extraction of feature vector are comprised of modified 2-D conditional moments based on estimated Gibbs distribution for an facial image. In the facial expression recognition phase, we use discrete left-right HMM which is widely used in pattern recognition. In order to evaluate the performance of the proposed scheme, experiments for recognition of four universal expression (anger, fear, happiness, surprise) was conducted with facial image sequences on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 95%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.271-272
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• 2016

본 논문에서는 딥러닝 기술 중의 하나인 CNN(Convolutional Neural Network) 기반의 얼굴 표정 인식 기법을 제안한다. 다섯 가지 주요 표정의 얼굴 영상을 CNN 구조에 스스로 학습시켜 각각의 표정 패턴에 적합한 특징 지도(feature map)를 형성하고 이 특징 지도를 통해 들어오는 입력 영상을 적합한 표정으로 분류한다. 기존의 CNN 구조를 본 논문에서 이용한 데이터 셋에 알맞게 convolutional layer 및 node의 수를 변경하여 특징 지도를 형성하고 학습 및 인식에 필요한 파라미터수를 대폭 감소시켰다. 실험 결과 제안하는 기법이 높은 얼굴 표정 분류 성능을 보여준다는 것을 보였다.

• Journal of KIISE
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• v.42 no.11
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• pp.1459-1466
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• 2015

Lockscreen is one of the most frequently encountered interfaces by smartphone users. Although users perform unlocking actions every day, there are no benefits in using lockscreens apart from security and authentication purposes. In this paper, we replace the traditional lockscreen with an application that analyzes facial expressions in order to collect facial expression data and provide real-time feedback to users. To evaluate this concept, we have implemented Quantified Lockscreen application, supporting the following contributions of this paper: 1) an unobtrusive interface for collecting facial expression data and evaluating emotional patterns, 2) an improvement in accuracy of facial expression detection through a personalized machine learning process, and 3) an enhancement of the validity of emotion data through bidirectional, multi-channel and multi-input methodology.

• Convergence Security Journal
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• v.14 no.7
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• pp.23-28
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• 2014

This study proposes the design and implementation of the system for a facial expression recognition system. LDP(Local Directional Pattern) feature computes the edge response in a different direction from a pixel with the relationship of neighbor pixels. It is necessary to be estimated that LDP code can represent facial features correctly under various conditions. In this respect, we build the system of facial expression recognition to test LDP performance quickly and the proposed evaluation system consists of six components. we experiment the recognition rate with local micro patterns (LDP, Gabor, LBP) in the proposed evaluation system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.614-623
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• 1994

Synthesizing speech and facial images is necessary for human interface that man and machine converse naturally as human do. The target of this paper is synthesizing the facial images. In synthesis of the facial images a three-dimensional (3-D) shape model of the face is used for realizating the facial expression variations and the lip shape variations. The various facial expressions and lip shapes harmonized with the syllables are synthesized by deforming the three-dimensional model on the basis of the facial muscular actions. Combications with the consonants and the vowels make 14.364 syllables. The vowels dominate most lip shapes but the consonants do a part of them. For determining the lip shapes, this paper investigates all the syllables and classifies the lip shapes pattern according to the vowels and the consonants. As the results, the lip shapes are classified into 8 patterns for the vowels and 2patterns for the consonants. In advance, the paper determines the synthesis rules for the classified lip shape patterns. This method permits us to obtain the natural facial image with the various facial expressions and lip shape patterns.