A Review of Active Appearance Models

Gao, Xinbo; Su, Ya; Li, Xuelong; Tao, Dacheng

doi:10.1109/tsmcc.2009.2035631

Cited by 112 publications

(25 citation statements)

References 86 publications

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“…The ASM method can be further enhanced by building an active appearance model (AAM) (Cootes et al, 1998;Gao et al, 2010) which uses all the available data instead of just the landmark neighbourhoods. A PDM also needs to be built, but appearance is now modelled by distorting each of the training images so that the landmarks defined in each match the mean shape.…”

Section: Appearance Modellingmentioning

confidence: 99%

Unsupervised segmentation of the vocal tract from real-time MRI sequences

Silva

Teixeira

2015

Computer Speech & Language

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Section: Appearance Modellingmentioning

confidence: 99%

Unsupervised segmentation of the vocal tract from real-time MRI sequences

Silva

Teixeira

2015

Computer Speech & Language

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“…In this section, three publicly available face datasets are selected for performance comparison: Labeled Face Parts in the Wild (LFPW-68 points and LFPW-29 points [16]) and Caltech Occluded Faces in the Wild 2013 (COFW) [34]. In the downloaded LFPW dataset, 811 training images and 224 testing images are collected.…”

Section: Methodsmentioning

confidence: 99%

A Robust Shape Reconstruction Method for Facial Feature Point Detection

Tan

Chen

Guo

et al. 2017

Computational Intelligence and Neuroscience

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Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods.

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“…Captures both shape and texture information, and image can be reconstructed from the extracted features. 54 Hence, it is suitable for modeling deformable objects.…”

Section: Aammentioning

confidence: 99%

Automatic age and gender classification using supervised appearance model

Bukar

Ugail

Connah

2016

J. Electron. Imaging

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YesAge and gender classification are two important problems that recently gained popularity in the\ud research community, due to their wide range of applications. Research has shown that both age and gender\ud information are encoded in the face shape and texture, hence the active appearance model (AAM), a statistical\ud model that captures shape and texture variations, has been one of the most widely used feature extraction\ud techniques for the aforementioned problems. However, AAM suffers from some drawbacks, especially when\ud used for classification. This is primarily because principal component analysis (PCA), which is at the core of\ud the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account\ud how the predictor variables relate to the response (class labels). Rather, it explores only the underlying structure\ud of the predictor variables, thus, it is no surprise if PCA discards valuable parts of the data that represent discriminatory\ud features. Toward this end, we propose a supervised appearance model (sAM) that improves on AAM\ud by replacing PCA with partial least-squares regression. This feature extraction technique is then used for the\ud problems of age and gender classification. Our experiments show that sAM has better predictive power than the\ud conventional AAM

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A Review of Active Appearance Models

Cited by 112 publications

References 86 publications

Unsupervised segmentation of the vocal tract from real-time MRI sequences

Unsupervised segmentation of the vocal tract from real-time MRI sequences

A Robust Shape Reconstruction Method for Facial Feature Point Detection

Automatic age and gender classification using supervised appearance model

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