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access icon free Extraction of informative regions of a face for facial expression recognition

© The Institution of Engineering and Technology
Received 17/07/2015, Accepted 01/02/2016, Revised 02/12/2015, Published 12/02/2016

The aim of facial expression recognition (FER) algorithms is to extract discriminative features of a face. However, discriminative features for FER can only be obtained from the informative regions of a face. Also, each of the facial subregions have different impacts on different facial expressions. Local binary pattern (LBP) based FER techniques extract texture features from all the regions of a face, and subsequently the features are stacked sequentially. This process generates the correlated features among different expressions, and hence affects the accuracy. This research moves toward addressing these issues. The authors' approach entails extracting discriminative features from the informative regions of a face. In this view, they propose an informative region extraction model, which models the importance of facial regions based on the projection of the expressive face images onto the neural face images. However, in practical scenarios, neutral images may not be available, and therefore the authors propose to estimate a common reference image using Procrustes analysis. Subsequently, weighted-projection-based LBP feature is derived from the informative regions of the face and their associated weights. This feature extraction method reduces miss-classification among different classes of expressions. Experimental results on standard datasets show the efficacy of the proposed method.

Inspec keywords: feature extraction; face recognition

Other keywords: JAFFE; weighted-projection-based LBP feature; facial expression recognition; local binary pattern; neutral face image; discriminative feature extraction method; informative region extraction model; Procrustes analysis; FER algorithm; Cohn–Kanade dataset; MUG

Subjects: Computer vision and image processing techniques; Image recognition

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