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, Tao Jiang, Linna Wang, Linna Wang, Xiaodong Zhao, 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); doi: 10.1117/12.2244606; https://doi.org/10.1117/12.2244606
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