中文摘要：

提出了一种基于分块局部二元模式（LBP）的鉴别特征抽取方法。该方法对人脸图像进行分块，再对分块后的子图像矩阵采用LBP算予抽取LBP特征。由于LBP是利用一串二进制码表征较小图像块的局部纹理，这有助于提高人脸识别的性能。采用主分量分析（PCA）方法对由所有分块后子图像的LBP特征向量构成的新训练集进行维度缩减，最后以Fisher线性鉴别分析（LDA）对缩减后的PCA特征进行鉴别特征提取。在ORL人脸库和YALE人脸库上的实验结果表明，该方法优于传统的PCA和LDA方法。

英文摘要：

This paper presents a new discriminant feature extraction method based bases on modular local binary pattern （LBP）. According to the proposed method, the original face images are firstly divided into smaller sub-images. Then, the LBP operator is applied to each of these sub-images and the effective texture feature is extracted. A new training set is formed by the LBP feature vector of each sub-image. The lower dimensional PCA-based features can be computed by applying PCA on the new training set. Finally, LDA is performed on the reduced PCA-based feature vectors. The experimental results on both ORL face database and YALE face database show that the proposed method is more effective than traditional PCA and LDA methods.