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29 August 2016 Facial expression recognition based on adaptively weighted improved local binary pattern
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100330W (2016) https://doi.org/10.1117/12.2244606
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
In order to fully describe the texture informations of the image, and to distinguish the sub-regions which contain different texture informations, this paper proposes a method of facial expression recognition based on adaptively weighted improved Local Binary Pattern (LBP). Firstly, the whole face region and expression sub-regions of eyebrows, eyes, nose and mouth are isolated by preprocessing. Secondly, the features of the sub-regions are extracted by improved LBP, the Fisher Linear Discriminant (FLD) is applied to calculated the weights of sub-regions, and then the weighted histograms of expression sub-regions are fused as the histogram of facial expression feature. Finally, the fused features are classified by Support Vector Machine (SVM). The experiments are performed on JAFFE and Extended Cohn-Kanada database(CK+), and the experimental results demonstrate that the proposed method has better recognition performance.
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Tao Jiang, Linna Wang, and Xiaodong Zhao "Facial expression recognition based on adaptively weighted improved local binary pattern", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100330W (29 August 2016); https://doi.org/10.1117/12.2244606
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