Extraction of informative regions of a face for facial expression recognition. Issue 6 (19th April 2016)

• Record Type: Journal Article
• Authors: Kumar, Sunil; Bhuyan, M.K.; Chakraborty, Biplab Ketan
• Is Part Of: IET computer vision. Volume 10:Issue 6(2016)
• Page Start: 567
• Page End: 576
• Publication Date: 2016-04-19
• Subjects: feature extraction -- face recognition; Cohn–Kanade dataset -- JAFFE -- MUG -- weighted-projection-based LBP feature -- Procrustes analysis -- neutral face image -- local binary pattern -- discriminative feature extraction method -- FER algorithm -- facial expression recognition -- informative region extraction model; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2015.0273
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.