Audio-visual feature selection and reduction for emotion classification
Haq, S, Jackson, PJB and Edge, J (2008) Audio-visual feature selection and reduction for emotion classification
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Recognition of expressed emotion from speech and facial gestures was investigated in experiments on an audio-visual emotional database. A total of 106 audio and 240 visual features were extracted and then features were selected with Plus l-Take Away r algorithm based on Bhattacharyya distance criterion. In the second step, linear transformation methods, principal component analysis (PCA) and linear discriminant analysis (LDA), were applied to the selected features and Gaussian classifiers were used for classification of emotions. The performance was higher for LDA features compared to PCA features. The visual features performed better than audio features, for both PCA and LDA. Across a range of fusion schemes, the audio-visual feature results were close to that of visual features. A highest recognition rate of 53% was achieved with audio features, 98% with visual features, and 98% with audio-visual features selected by Bhattacharyya distance and transformed by LDA.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Copyright 2008 ISCA|
|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Depositing User:||Symplectic Elements|
|Date Deposited:||21 Mar 2012 12:57|
|Last Modified:||23 Sep 2013 18:50|
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