基于多尺度局部二值模式的人脸识别

提出了一种基于多尺度局部二值模式的人脸识别方法.局部二值模式已经被证明是人脸表示的一种有效算子,不过由于其太小以至于鲁棒性不高.在多尺度局部二值模式中,计算是基于块子区域的平均值,而不是基于单个像素值进行的.人脸图像首先被分成小的子区域,具有不同权值的BLBP算子抽取每一子区域的直方图,然后把它们连接起来,组成一个空域增强的特征直方图.在X~2统计量作为不相似度量计算的特征空间里,采用最近邻分类器完成分类识别.实验表明,该方法优于其它的基于LBP的人脸识别算法.

Face Recognition Based on Multi-scale Block Local Binary Pattern

We proposed a face recognition representation method, called multi scale block Local Binary Pattern. The Local Binary Pattern(LBP) was proved to be effective for image representation,but it was too local to be robust. In MS-BLBP, the computation was done based on average values of block subregions, instead of individual pixels. The face area was first divided into small regions from which Block Local Binary Patterns (BLBP) with different weights histograms were extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The classification was performed using a nearest neighbour classifier in the computed feature space with Chi square as a dissimilarity measure. Experiments in face databases show that the proposed MS-BLBP method outperforms other LBP based face recognition algorithms.