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31 December 2019 Face recognition based on most value averageing LBP and gray level co-occurrence matrix
Bo Fu, Chao Xu, Xilin Zhao, Guanghui Xu, Na Fang
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Proceedings Volume 11384, Eleventh International Conference on Signal Processing Systems; 113841A (2019) https://doi.org/10.1117/12.2559763
Event: Eleventh International Conference on Signal Processing Systems, 2019, Chengdu, China
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.
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Bo Fu, Chao Xu, Xilin Zhao, Guanghui Xu, and Na Fang "Face recognition based on most value averageing LBP and gray level co-occurrence matrix", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113841A (31 December 2019); https://doi.org/10.1117/12.2559763
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KEYWORDS
Detection and tracking algorithms
Databases
Feature extraction
Facial recognition systems
Image compression
Principal component analysis
Binary data
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