Active appearance models

Cootes, Timothy F.; Edwards, Gillian; Taylor, Chris

doi:10.1109/34.927467

Cited by 4,824 publications

(2,251 citation statements)

References 13 publications

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“…, x n , y n ] T from these annotations. After applying Principle Component Analysis (PCA) [2], a morphable shape model is constructed as…”

Section: Shape and Texture Models In Active Appearance Models [2]mentioning

confidence: 99%

“…Active Appearance Models (AAM) [2] are composed of a shape model, a texture model, and a fitting method. Boosting Appearance Models (BAM) [5] propose a more discriminative method via rectangular Haar-like features and boosting.…”

Section: Boosting Appearance Modelsmentioning

confidence: 99%

“…Inspired by Active Appearance Models (AAM) [2], the morphable face model is generated from a set of facial images. From a giving face database, we manually label a series of 2D annotations {x i , y i }, i = 1, 2, .…”

Section: Shape and Texture Models In Active Appearance Models [2]mentioning

confidence: 99%

“…Among the various technologies of face alignment, Active Shape Models (ASM) [1] and Active Appearance Models (AAM) [2] have gradually taken the stage center. ASM utilized the local texture information in search of a better template, and AAM constructed appearance models according to shape parameters and global texture constraints.…”

Section: Introductionmentioning

confidence: 99%

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Face Alignment Using Boosting and Evolutionary Search

Zhang

Liu

Poel

et al. 2010

Computer Vision – ACCV 2009

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Abstract. In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images.

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“…, x n , y n ] T from these annotations. After applying Principle Component Analysis (PCA) [2], a morphable shape model is constructed as…”

Section: Shape and Texture Models In Active Appearance Models [2]mentioning

confidence: 99%

Section: Boosting Appearance Modelsmentioning

confidence: 99%

Section: Shape and Texture Models In Active Appearance Models [2]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Face Alignment Using Boosting and Evolutionary Search

Zhang

Liu

Poel

et al. 2010

Computer Vision – ACCV 2009

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“…Face alignment with partial face images is a prerequisite for solving this problem. Popular face alignment methods are mostly based on holistic face model, such as Active Shape Model (ASM) [1], Active Appearance Model (AAM) [2]. When the integrality of faces cannot be guaranteed, these holistic models will lose their power.…”

Section: Introductionmentioning

confidence: 99%

Automatic Partial Face Alignment in NIR Video Sequences

Yang

Liao

2009

Advances in Biometrics

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Abstract. Face recognition with partial face images is an important problem in face biometrics. The necessity can arise in not so constrained environments such as in surveillance video, or portal video as provided in Multiple Biometrics Grand Challenge (MBGC). Face alignment with partial face images is a key step toward this challenging problem.In this paper, we present a method for partial face alignment based on scale invariant feature transform (SIFT). We first train a reference model using holistic faces, in which the anchor points and their corresponding descriptor subspaces are learned from initial SIFT keypoints and the relationships between the anchor points are also derived. In the alignment stage, correspondences between the learned holistic face model and an input partial face image are established by matching keypoints of the partial face to the anchor points of the learned face model. Furthermore, shape constraint is used to eliminate outlier correspondences and temporal constraint is explored to find more inliers. Alignment is finally accomplished by solving a similarity transform. Experiments on the MBGC near infrared video sequences show the effectiveness of the proposed method, especially when PCA subspace, shape and temporal constraint are utilized.

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Face Recognition Via Sparse Representation

Yang

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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Active appearance models

Cited by 4,824 publications

References 13 publications

Face Alignment Using Boosting and Evolutionary Search

Face Alignment Using Boosting and Evolutionary Search

Automatic Partial Face Alignment in NIR Video Sequences

Face Recognition Via Sparse Representation

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