2D facial expression recognition via 3D reconstruction and feature fusion |
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Affiliation: | 1. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran;2. Electrical Engineering Department, Semnan University, Semnan, Iran;1. School of Electrical Engineering, Korea University, Seoul, Republic of Korea;2. Department of Electronics Engineering, Ewha Womans University, Seoul 120-750, Republic of Korea |
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Abstract: | In this paper, a novel feature extraction method is proposed for facial expression recognition by extracting the feature from facial depth and 3D mesh alongside texture. Accordingly, the 3D Facial Expression Generic Elastic Model (3D FE-GEM) method is used to reconstruct an expression-invariant 3D model from the human face. Then, the texture, depth and mesh are extracted from the reconstructed face model. Afterwards, the Local Binary Pattern (LBP), proposed 3D High-Low Local Binary Pattern (3DH-LLBP) and Local Normal Binary Patterns (LNBPs) are applied to texture, depth and mesh of the face, respectively, to extract the feature from 2D images. Finally, the final feature vectors are generated through feature fusion and are classified by the Support Vector Machine (SVM). Convincing results are acquired for facial expression recognition on the CK+, CK, JAFFE and Bosphorus image databases compared to several state-of-the-art methods. |
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Keywords: | Facial expression recognition Facial expression generic elastic model 3D local binary pattern Local normal binary pattern Expression-invariant 3D face reconstruction Local binary pattern Feature fusion 2D and 3D features |
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