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.

中文翻译：

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一种基于局部二值模式预处理和小波变换的特征提取方法

近几十年来，人脸识别已被许多学者广泛研究。局部二进制模式（LBP）是最流行的局部描述符之一，已广泛应用于人脸识别。小波变换在模式识别领域也越来越活跃。在本文中，提出了一种新的特征提取方法来克服光照影响。首先，给定的人脸图像经过LBP算子处理，得到一张LBP图像。其次，小波变换用于从LBP图像中提取判别特征。在 LFW、Extended YaleB 和 CMU-PIE 人脸数据库上的实验结果表明，所提出的方法优于几种流行的人脸识别方法，并且预处理步骤对于提取有效特征进行分类具有重要作用。