基于ULBP特征子空间的2DLDA人脸识别方法

Face Recognition of 2DLDA Based on ULBP Eigensubspace

将图像层次化分割并提取各个图像子块的均匀模式的局部二值模式(ULBP)直方图特征,在考虑到全局及局部特征的同时,将处理空间从灰度空间投影到ULBP特征子空间,有效消除行向量之间的相关性,从而使应用行二维线性鉴别分析处理得到的鉴别投影矩阵性能更优.在ORL、YALE及FERET人脸库上与基于二维线性鉴别分析的方法及基于多级局部二值模式的方法对比,结果显示文中方法维数更低,识别率更高,从而验证文中方法的有效性.

The image is segmented at different levels to extract the uniform local binary pattern （ULBP） histogram features of the sub-block images. The global and local features are taken into account, and meanwhile the processing space is converted from the gray space to ULBP feature subspace. Consequently, the correlation between row vectors can be eliminated effectively. Thus, the discriminant projection matrix is performed better through row two-dimensional linear discriminant analysis （R2DLDA）. Experimental results on ORL, YALE and FERET databases show that compared with some common methods based on 2DLDA and multilevel LBP, the proposed method achieves a higher recognition rate with a low feature dimension, which proves its effectiveness.