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Face recognition based on most value averageing LBP and gray level co-occurrence matrix
Author(s): Bo Fu; Chao Xu; Xilin Zhao; Guanghui Xu; Na Fang
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Paper Abstract

In order to solve the problem that local binary pattern (LBP) is easy to lose some details when extracting facial features and image rotation leads to low recognition rate, a most value averaging LBP combined with gray level co-occurrence matrix feature algorithm is proposed. The method uses the most value averaging LBP algorithm to extract image features and reduces the feature dimension by principal component analysis (PCA); at the same time, considering the gray level co-occurrence matrix feature of the image, the most value averaging LBP feature is combined with the gray level cooccurrence matrix feature, and the k-nearest neighbor method (KNN) is used to classify and identify the face in lowdimensional space. The experimental results show that the proposed method has a good recognition effect.

Paper Details

Date Published: 31 December 2019
PDF: 10 pages
Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113841A (31 December 2019); doi: 10.1117/12.2559763
Show Author Affiliations
Bo Fu, Hubei Univ. of Technology (China)
Chao Xu, Hubei Univ. of Technology (China)
Xilin Zhao, Hubei Univ. of Technology (China)
Guanghui Xu, Hubei Univ. of Technology (China)
Na Fang, Hubei Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11384:
Eleventh International Conference on Signal Processing Systems
Kezhi Mao, Editor(s)

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