Tailoring Model-Based Techniques to Facial Expression Interpretation

Wimmer, Matthias; Mayer, Christoph; Pietzsch, Sylvia; Radig, Bernd

doi:10.1109/achi.2008.7

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Numerous methodologies have been proposed for facial expression analysis from both static images and image sequences [3], [4], [21], [23][24][25][26]. This study presents a computationally efficient approach to feature extraction and classification for facial expression recognition from static facial images.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using Neural Networks and Log-Gabor Filters

Lajevardi

Lech

2008

2008 Digital Image Computing: Techniques and Applications

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This study proposes a classification-based facial expression recognition method using a bank of multilayer perceptron neural networks. Six different facial expressions were considered. Firstly, logarithmic Gabor filters were applied to extract the features. Optimal subsets of features were then selected for each expression, down-sampled and further reduced in size via Principal Component Analysis (PCA). The arrays of eigenvectors were multiplied by the original log-Gabor features to form feature arrays concatenated into six data tensors, representing training sets for different emotions. Each tensor was then used to train one of the six parallel neural networks making each network most sensitive to a different emotion. The classification efficiency of the proposed method was tested on static images from the Cohn-Kanade database. The results were compared with the full set of log-Gabor features. The average percentage of the correct classifications varied across different expressions from 31% to 85% for the optimised sub-set of log-Gabor features and from 23% to 67% for the full set of features. The average correct classification rate was increased from 52% for the full set of the log-Gabor features, to 70% for the optimised sub-set of log-Gabor features.

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Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Using Neural Networks and Log-Gabor Filters

Lajevardi

Lech

2008

2008 Digital Image Computing: Techniques and Applications

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“…The initial parameter estimates are usually provided by a global image search routine such as [17,18,12]. Most Figure 1.…”

Section: Related Approaches To Initializing Aam Fittingmentioning

confidence: 99%

An ASM fitting method based on machine learning that provides a robust parameter initialization for AAM fitting

Wimmer

Fujie

Stulp

et al. 2008

2008 8th IEEE International Conference on Automatic Face &Amp; Gesture Recognition

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Due to their use of information contained in texture, Active Appearance Models (AAM) generally outperform Active Shape Models (ASM) in terms of fitting accuracy. Although many extensions and improvements over the original AAM have been proposed, on of the main drawbacks of AAMs remains its dependence on good initial model parameters to achieve accurate fitting results. In this paper, we determine the initial model parameters for AAM fitting with ASM fitting, and use machine learning techniques to improve the scope and accuracy of ASM fitting. Combining the precision of AAM fitting with the large radius of convergence of learned ASM fitting improves the results by an order of magnitude, as our empirical evaluation on a database of publicly available benchmark images demonstrates.

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Averaged Gabor Filter Features for Facial Expression Recognition

Lajevardi

Lech

2008

2008 Digital Image Computing: Techniques and Applications

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Tailoring Model-Based Techniques to Facial Expression Interpretation

Cited by 4 publications

References 22 publications

Facial Expression Recognition Using Neural Networks and Log-Gabor Filters

Facial Expression Recognition Using Neural Networks and Log-Gabor Filters

An ASM fitting method based on machine learning that provides a robust parameter initialization for AAM fitting

Averaged Gabor Filter Features for Facial Expression Recognition

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