Face recognition based on most value averageing LBP and gray level co-occurrence matrix

Bo Fu; Chao Xu; Xilin Zhao; Guanghui Xu; Na Fang

doi:10.1117/12.2559763

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

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.

Citation Download Citation

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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