A Feature Extraction Method based on Local Binary Pattern Preprocessing and Wavelet Transform

Citations Over Time

Abstract

Face recognition has been extensively studied by many scholars in the recent decades. Local binary pattern (LBP) is one of the most popular local descriptors and has been widely applied to face recognition. Wavelet transform is also more and more active in the field of pattern recognition. In this paper, a novel feature extraction method is proposed to overcome illumination influence. First, a given face image is processed by the LBP operator, and an LBP image is obtained. Second, wavelet transform is used to extract discriminant feature from the LBP image. The experiment results on LFW, Extended YaleB and CMU-PIE face databases show that the proposed method outperforms several popular face recognition methods, and the preprocessing step plays an important role to extract effective features for classification.

Related Papers

• → Fusing Gabor and LBP feature sets for KNN and SRC-based face recognition(2016)29 cited
• → Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis(2020)20 cited
• Thermal Infrared Face Image Recognition Based on PCA and LDA(2008)
• → Profile Face Recognition using Local Binary Patterns with Artificial Neural Network(2018)6 cited
• → A Hybrid Approach to Face Recognition Using Generalized Two-Dimensional Fisher's Linear Discriminant Method(2010)3 cited