• 한국HCI학회:학술대회논문집
• /
• 2007.02a
• /
• pp.1006-1011
• /
• 2007

Active Appearance Models은 객체의 모델링에 널리 사용되며, 특히 얼굴 모델은 얼굴 추적, 포즈 인식, 표정 인식, 그리고 얼굴 인식에 널리 사용되고 있다. 최초의 AAM은 Shape과 Appearance가 하나의 계수에 의해서 만들어 지는 Combined AAM이였고, 이후 Shape과 Appearance의 계수가 분리된 Independent AAM과 3D를 표현할 수 있는 Combined 2D+3D AAM이 개발 되었다. 비록 Combined 2D+3D AAM이 3D를 표현 할 수 있을지라도 이들은 공통적으로 2D 영상을 사용하여 모델을 생산한다. 본 논문에서 우리는 stereo-camera based 3D face capturing device를 통해 획득한 3D 데이터를 기반으로 하는 3D AAM을 제안한다. 우리의 3D AAM은 3D정보를 이용해 모델을 생산하므로 기존의 AAM보다 정확한 3D표현이 가능하고 Alignment Algorithm으로 Inverse Compositional Image Alignment(ICIA)를 사용하여 빠르게 Model Instance를 생산할 수 있다. 우리는 3D AAM을 평가하기 위해 stereo-camera based 3D face capturing device로 촬영해 수집한 한국인 얼굴 데이터베이스[9]로 얼굴인식을 수행하였다.

• The Journal of the Korea Contents Association
• /
• v.7 no.2
• /
• pp.11-20
• /
• 2007

AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In this paper, we propose a face alignment method using progressive AAM. The proposed method consists of two stages; modelling and relation derivation stage and fitting stage. Modelling and relation derivation stage first builds two AAM models; the inner face AAM model and the whole face AAM model and then derive the relation matrix between the inner face AAM model parameter vector and the whole face AAM model parameter vector. The fitting stage is processed progressively in two phases. In the first phase, the proposed method finds the feature parameters for the inner facial feature points of a new face, and then in the second phase it localizes the whole facial feature points of the new face using the initial values estimated utilizing the inner feature parameters obtained in the first phase and the relation matrix obtained in the first stage. Through experiments, it is verified that the proposed progressive AAM-based face alignment method is more robust with respect to pose, and face background than the conventional basic AAM-based face alignment.

• 한국방재학회:학술대회논문집
• /
• 2007.02a
• /
• pp.538-541
• /
• 2007

In this study, the complex AAM permanent anchor was introduced and the design method for the complex AAM permanent anchor was showed by examining the relationship of the forces applied to the anchor, the ground failure, the loads inducing the tensile failure between the anchors, etc. In order to understand the behavioral characteristics of the complex AAM permanent anchor, the field pullout test was carried out, and the results obtained with the design method were compared with those of the field pullout test.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.8
• /
• pp.1201-1206
• /
• 2013

Active Appearance Model(AAM) is one of the facial feature extraction techniques. In this paper, we propose the Multiple Active Appearance Model(MAAM). Proposed method uses two AAMs. Each AAM trains using different training parameters. It causes that each AAM has different strong points. One AAM complements the weak points in the other AAM. We performed the facial feature extraction on the 100 images to verify the performance of MAAM. Experiment results show that MAAM gives more accurate results than AAM with less fitting iteration.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.6
• /
• pp.834-839
• /
• 2009

It is a key element that the problem of emotional feature extraction based on facial image to recognize a human emotion status. In this paper, we propose an Advanced AAM that is improved version of proposed Facial Expression Recognition Systems based on Bayesian Network by using FACS and AAM. This is a study about the most efficient method of optimal facial feature area for human emotion recognition about random user based on generalized HCI system environments. In order to perform such processes, we use a Statistical Shape Analysis at the normalized input image by using Advanced AAM and FACS as a facial expression and emotion status analysis program. And we study about the automatical emotional feature extraction about random user.

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

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.197-202
• /
• 2009

Active Appearance Models(AAMs)은 얼굴인식, 얼굴추적, 표정인식 뿐만 아니라 눈동자 추적과 같은 분야에도 적용되어 좋은 성능을 보여 주었다. 보통 AAM 을 생성하기 위해서는 얼굴 영상과 얼굴의 특징을 나타내는 점으로 구성된 매쉬로 이루어 지는 트레이닝 셋이 필요하다. AAM fitting algorithm 은 학습한 얼굴과 유사한 얼굴을 Fitting 할 때에는 뛰어난 성능을 보이지만 조명에 의한 그림자 또는 액세서리에 의한 얼굴의 피부 가림과 같이 전체 얼굴이 잘 나타나지 않는 불완전한 영상의 Fitting 은 입력영상과 템플릿 영상간의 오차가 커지기 때문에 실패할 가능성이 매우 높다. 본 논문에서 우리는 AAMs 에서 사용되는 PCA를 Higher-order Singular Value Decomposition(HOSVD)로 대체하여 이 문제를 보완하는 강화된 AAM 을 제안한다. 제안된 AAM 에는 기존에 사용하던 고유벡터와 함께 HOSVD 를 통해 획득할 수 있는 Eigen-Modes 를 추가하여 사용한다. 또한 우리는 Yale Face Database를 이용한 평가를 통해 제안된 AAM 이 기존 AAM 보다 불완전한 영상에 효과적으로 대응하는 것을 보여준다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.10c
• /
• pp.409-414
• /
• 2007

Active Appearance Model is a well-known model that can represent a non-rigid object effectively. However, since it uses the fixed appearance model, the fitting results are often unsatisfactory when the imaging condition of the target image is different from that of training images. To alleviate this problem, incremental AAM was proposed which updates its appearance bases in an on-line manner. However, it cannot deal with the sudden changes of illumination. To overcome this, we propose a novel scheme to update the appearance bases. When a new person appears in the input image, we synthesize illuminated images of that person and update the appearance bases of AAM using it. Since we update the appearance bases using synthesized illuminated images in advance, the AAM can fit their model to a target image well when the illumination changes drastically. The experimental results show that our proposed algorithm improves the fitting performance over both the incremental AAM and the original AAM.

• Elastomers and Composites
• /
• v.57 no.2
• /
• pp.48-54
• /
• 2022

A polymeric adhesion promoter was synthesized to improve the adhesive strength of the Ni lead frame/epoxy composite. Poly(itaconic acid-co-acrylamide) (IAcAAM) was prepared by copolymerizing itaconic acid and acrylamide. We compared the adhesive strength between the Ni lead frame and epoxy composite according to the molecular weight of IAcAAM. The molecular weight of IAcAAM was controlled using an initiator, which made it possible to use IAcAAM in the epoxy molding compound (EMC) manufacturing process by modulating the melting temperature. The adhesive strength of Ni lead frame/epoxy composite increased with the addition of IAcAAM to the epoxy composite. In addition, as the molecular weight of IAcAAM increased, the adhesive strength of the Ni lead frame/epoxy composite slightly increased. We confirmed that IAcAAM with an appropriate molecular weight can be used in the EMC manufacturing process and increase the adhesive strength of the Ni lead frame/epoxy composite.

• The Journal of the Korea Contents Association
• /
• v.7 no.2
• /
• pp.1-10
• /
• 2007

In this paper, we propose a face recognition system using AAM and Gabor feature vectors. EBGM, which is prominent among face recognition algorithms employing Gabor feature vectors, requires localization of facial feature points where Gabor feature vectors are extracted. However, localization of facial feature points employed in EBGM is based on Gator jet similarity and is sensitive to initial points. Wrong localization of facial feature points affects face recognition rate. AAM is known to be successfully applied to localization of facial feature points. In this paper, we propose a facial feature point localization method which first roughly estimate facial feature points using AAM and refine facial feature points using Gabor jet similarity-based localization method with initial points set by the facial feature points estimated from AAM, and propose a face recognition system based on the proposed localization method. It is verified through experiments that the proposed face recognition system using the combined localization performs better than the conventional face recognition system using the Gabor similarity-based localization only like EBGM.