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Feature extraction of face image based on LBP and 2-D Gabor wavelet transform

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

Special Issues: Engineering Applications of Artificial Intelligence

Affected by illumination, gesture, expression and other factor’s variation, face image pattern is easy to be changed, so it is important to find a robust data representation for the correct classification of face pattern. In this paper, a face image recognition algorithm based on 2-D Gabor wavelet transform and Local Binary Pattern (LBP) is proposed. LBP is a local describe operator, which is invariant against illumination variation. 2-D Gabor wavelet transform have the invariant property against pose and expression variation. Experimental results show that the large scale 2-D Gabor wavelet representation could get good classification accuracy. Using LBP to describe 2-D Gabor wavelet representation of face image, together with image block, histogram statistics, PCA dimensionality reduction, nearestneighbors classification, we finally find this algorithm can get a better classification performance in different scales and directions.
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Keywords face recognition; local binary pattern; 2-D Gabor wavelet transform; features extraction

Citation: Qian Zhang, Haigang Li, Ming Li, Lei Ding. Feature extraction of face image based on LBP and 2-D Gabor wavelet transform. Mathematical Biosciences and Engineering, 2020, 17(2): 1578-1592. doi: 10.3934/mbe.2020082


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