An adaptive weighted fusion model with two subspaces for facial expression recognition |
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Authors: | Zhe Sun Zheng-ping Hu Raymond Chiong Meng Wang Shuhuan Zhao |
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Affiliation: | 1.School of Information Science and Engineering,Yanshan University,Qinhuangdao,China;2.School of Physics and Electronic Engineering,Taishan University,Tai’an,China;3.School of Electrical Engineering and Computing,The University of Newcastle,Callaghan,Australia;4.School of Information Science and Engineering,Hebei University,Baoding,China |
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Abstract: | Automatic facial expression recognition has received considerable attention in the research areas of computer vision and pattern recognition. To achieve satisfactory accuracy, deriving a robust facial expression representation is especially important. In this paper, we present an adaptive weighted fusion model (AWFM), aiming to automatically determine optimal weighted values. The AWFM integrates two subspaces, i.e., unsupervised and supervised subspaces, to represent and classify query samples. The unsupervised subspace is formed by differentiated expression samples generated via an auxiliary neutral training set. The supervised subspace is obtained through the reconstruction of intra-class singular value decomposition based on low-rank decomposition from raw training data. Our experiments using three public facial expression datasets confirm that the proposed model can obtain better performance compared to conventional fusion methods as well as state-of-the-art methods from the literature. |
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