• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.1012-1018
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• 2007

본 논문에서는 비전 기반 3차원 얼굴 모델의 자동 표정 생성 시스템을 제안한다. 기존의 3차원 얼굴 애니메이션에 관한 연구는 얼굴의 움직임을 나타내는 모션 추정을 배제한 얼굴 표정 생성에 초점을 맞추고 있으며 얼굴 모션 추정과 표정 제어에 관한 연구는 독립적으로 이루어지고 있다. 제안하는 얼굴 모델의 표정 생성 시스템은 크게 얼굴 검출, 얼굴 모션 추정, 표정 제어로 구성되어 있다. 얼굴 검출 방법으로는 얼굴 후보 영역 검출과 얼굴 영역 검출 과정으로 구성된다. HT 컬러 모델을 이용하며 얼굴의 후보 영역을 검출하며 얼굴 후보 영역으로부터 PCA 변환과 템플릿 매칭을 통해 얼굴 영역을 검출하게 된다. 검출된 얼굴 영역으로부터 얼굴 모션 추정과 얼굴 표정 제어를 수행한다. 3차원 실린더 모델의 투영과 LK 알고리즘을 이용하여 얼굴의 모션을 추정하며 추정된 결과를 3차원 얼굴 모델에 적용한다. 또한 영상 보정을 통해 강인한 모션 추정을 할 수 있다. 얼굴 모델의 표정을 생성하기 위해 특징점 기반의 얼굴 모델 표정 생성 방법을 적용하며 12개의 얼굴 특징점으로부터 얼굴 모델의 표정을 생성한다. 얼굴의 구조적 정보와 템플릿 매칭을 이용하여 눈썹, 눈, 입 주위의 얼굴 특징점을 검출하며 LK 알고리즘을 이용하여 특징점을 추적(Tracking)한다. 추적된 특징점의 위치는 얼굴의 모션 정보와 표정 정보의 조합으로 이루어져있기 때문에 기하학적 변환을 이용하여 얼굴의 방향이 정면이었을 경우의 특징점의 변위인 애니메이션 매개변수를 획득한다. 애니메이션 매개변수로부터 얼굴 모델의 제어점을 이동시키며 주위의 정점들은 RBF 보간법을 통해 변형한다. 변형된 얼굴 모델로부터 얼굴 표정을 생성하며 모션 추정 결과를 모델에 적용함으로써 얼굴 모션 정보가 결합된 3차원 얼굴 모델의 표정을 생성한다.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3211-3218
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• 1997

This paper addresses an eye location algorithm which is essential process of human face tracking system for natural video-conferencing. In current video-conferencing systems, user's facial movements are restricted by fixed camera, therefore it is inconvenient to users. We Propose an eye location algorithm for automatic face tracking. Because, locations of other facial features guessed from locations of eye and scale of face in the image can be calculated using inter-ocular distance. Most previous feature extraction methods for face recognition system are approached under assumption that approximative face region or location of each facial feature is known. The proposed algorithm in this paper uses no prior information on the given image. It is not sensitive to backgrounds and lighting conditions. The proposed algorithm uses the valley representation as major information to locate eyes. The experiments have been performed for 213 frames of 17 people and show very encouraging results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1071-1076
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• 2006

In this paper, we develop face and hand tracking for sign language recognition system. The system is divided into two stages; the initial and tracking stages. In initial stage, we use the skin feature to localize face and hands of signer. The ellipse model on CbCr space is constructed and used to detect skin color. After the skin regions have been segmented, face and hand blobs are defined by using size and facial feature with the assumption that the movement of face is less than that of hands in this signing scenario. In tracking stage, the motion estimation is applied only hand blobs, in which first and second derivative are used to compute the position of prediction of hands. We observed that there are errors in the value of tracking position between two consecutive frames in which velocity has changed abruptly. To improve the tracking performance, our proposed algorithm compensates the error of tracking position by using adaptive search area to re-compute the hand blobs. The experimental results indicate that our proposed method is able to decrease the prediction error up to 96.87% with negligible increase in computational complexity of up to 4%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1816-1825
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• 2022

In this paper, we propose a new approach for tracking each human in a surveillance camera network with various resolution cameras. When tracking human on multiple non-overlapping cameras, the traditional appearance features are easily affected by various camera viewing conditions. To overcome this limitation, the proposed system utilizes facial information along with appearance information. In general, human images captured by the surveillance camera are often low resolution, so it is necessary to be able to extract useful features even from low-resolution faces to facilitate tracking. In the proposed tracking scheme, texture-based face descriptor is exploited to extract features from detected face after face frontalization. In addition, when the size of the face captured by the surveillance camera is very small, a super-resolution technique that enlarges the face is also exploited. The experimental results on the public benchmark Dana36 dataset show promising performance of the proposed algorithm.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.349-349
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• 2004

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.159-170
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• 2009

It is very important to extract the expression data and capture a face image from a video for online-based 3D face animation. In recently, there are many researches on vision-based approach that captures the expression of an actor in a video and applies them to 3D face model. In this paper, we propose an automatic data extraction system, which extracts and traces a face and expression data from realtime video inputs. The procedures of our system consist of three steps: face detection, face feature extraction, and face tracing. In face detection, we detect skin pixels using YCbCr skin color model and verifies the face area using Haar-based classifier. We use the brightness and color information for extracting the eyes and lips data related facial expression. We extract 10 feature points from eyes and lips area considering FAP defined in MPEG-4. Then, we trace the displacement of the extracted features from continuous frames using color probabilistic distribution model. The experiments showed that our system could trace the expression data to about 8fps.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1089-1098
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• 2009

Recently, many researches of making more comfortable input device based on gaze detection technology have been performed in human computer interface. Previous researches were performed on the computer environment with a large sized monitor. With recent increase of using mobile device, the necessities of interfacing by gaze detection on mobile environment were also increased. In this paper, we research about the gaze detection method by using UMPC (Ultra-Mobile PC) and an embedded camera of UMPC based on face and facial feature detection by AAM (Active Appearance Model). This paper has following three originalities. First, different from previous research, we propose a method for tracking user's gaze position in mobile device which has a small sized screen. Second, in order to detect facial feature points, we use AAM. Third, gaze detection accuracy is not degraded according to Z distance based on the normalization of input features by using the features which are obtained in an initial user calibration stage. Experimental results showed that gaze detection error was 1.77 degrees and it was reduced by mouse dragging based on the additional facial movement.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.401-403
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• 1999

본 논문에서 우리는 제약이 없는 배경화면에서 얼굴의 움직임을 이용한 응시점 추적을 위해 얼굴의 특징점(눈, 코, 그리고 입)들을 찾고 head orientation을 구하는 효？거이고 빠른 방법을 제안한다. 얼굴을 찾는 방법이 많이 연구 되어 오고 있으나 많은 부분이 효과적이지 못하거나 제한적인 사항을 필요로 한다. 본 논문에서 제안한 방법은 이진화된 이미지에 기초하고 완전 그래프 매칭을 이용한 유사성을 구하는 방법이다. 즉, 임의의 임계치 값에 의해 이진화된 이미지를 레이블링 한 후 각 쌍의 블록에 대한 유사성을 구한다. 이때 두 눈과 가장 유사성을 갖는 두 블록을 눈으로 선택한다. 눈을 찾은 후 입과 코를 찾아간다. 360$\times$240 이미지의 평균 처리 속도는 0.2초 이내이고 다음 탐색영역을 예상하여 탐색 영역을 줄일 경우 평균 처리속도는 0.15초 이내였다. 그리고 본 논문에서는 얼굴의 움직임을 구하기 위해 각 특징점들이 이루는 각을 기준으로 한 템플릿 매칭을 이용했다. 실험은 다양한 조명환경과 여러 사용자를 대상으로 이루어졌고 속도와 정확성면에서 좋은 결과를 보였다. 도한, 명안정보만을 사용하므로 흑백가메라에서도 사용가능하여 경제적 효과도 기대할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.9
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• pp.1972-1978
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• 2010

AAM(Active Appearance Model) is one of the most effective ways to detect deformable 2D objects and is a kind of mathematical optimization methods. The cost function is a convex function because it is a least-square function, but the search space is not convex space so it is not guaranteed that a local minimum is the optimal solution. That is, if the initial value does not depart from around the global minimum, it converges to a local minimum, so it is difficult to detect face contour correctly. In this study, an AAM-based face tracking algorithm is proposed, which is robust to various lighting conditions and backgrounds. Eye detection is performed using SIFT and Genetic algorithm, the information of eye are used for AAM's initial matching information. Through experiments, it is verified that the proposed AAM-based face tracking method is more robust with respect to pose and background of face than the conventional basic AAM-based face tracking method.