Person-Independent Facial Expression Recognition Based on Improved Local Binary Pattern and Higher-Order Singular Value Decomposition

Person-Independent Facial Expression Recognition Based on Improved Local Binary Pattern and Higher-Order Singular Value Decomposition
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基于改进局部二值模式和高阶奇异值分解的与人无关的面部表情识别

DOI:
10.1109/access.2020.3032406
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发表时间:
2020-10
期刊:
影响因子:
3.9
通讯作者:
Shuxin Chen
Shuxin Chen
中科院分区:
计算机科学3区
文献类型:
--
作者:
Ying He;Shuxin Chen

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The recognition rate of person-independent facial expression is generally not high, which limits the practical application of facial expression recognition. Aiming at this problem, this paper analyzes the reasons for the low recognition rate of person-independent facial expression, and proposes a recognition algorithm of person-independent facial expression based on improved LBP (Local Binary Pattern) and HOSVD (Higher-Order Singular Value Decomposition). The algorithm has the following contributions of facial expression recognition framework. In the stage of facial expression feature extraction, the transient features extracted by LDP(Local Directional Pattern) and the persistent features extracted by CBP(Centralized Binary Pattern) are integrated to improve the discrimination of facial expression features. Moreover, in the stage of facial expression classification and recognition, the traditional nearest neighbor classification is changed into $k$ -nearest neighbor pre-classification, and the regional energy calculated by HOSVD is used to determine the similarity of two images for secondary classification. Finally, in the extended Cohn-Kanade dataset and Oulu-CASIA NIR&VIS facial expression database, the theoretical analysis and experimental results show that the method has better recognition effect for solving the problem of person-independent facial expression recognition.
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