分块二维局部保持鉴别分析在人脸识别中的应用

Modular Two-dimensional Locality Preserving Discriminant Analysis and its Application in Human Face Recognition

局部保持鉴别分析在人脸识别研究中具有非常重要的地位。在此基础上提出的2DLPDA算法直接在二维空间进行运算,一定程度上提高了性能。但是当样本在光照阴影、遮挡等情况下时,识别率受到很大影响,为此提出一种改进的算法,即分块二维局部保持鉴别分析方法。其将样本分块,以更好地提取样本中的局部近邻特征。这样同一样本的不同分块在选择近邻时,就可能具有来自不同样本的近邻,从而能更好地提取样本的局部特征。最后将局部特征整合为整体作为识别的依据。在AR、YALE及ORL库上验证了算法的有效性。

Locality preserving discriminant analysis takes very important position in face recognition research. Based on this,the 2DLPDA method was proposed,which directly processes the operation in the two dimensional space. In some way, it improves the performance of the algorithm. But the problem of the sensitivity to such variations like lighting＇ expression and occlusion will make a big influence on the recognition rate when using 2DLPDA method. We proposed an improved algorithm called modular ,wo-dimensional locality preserving discriminant analysis method. We divided the sample in blocks, so that we could extract the local neighborhood of the sample better. Because each sample was divided into blocks, the different blocks of one sample may have different nearest neighbors, causing local features of the sample to be extracted better. At the end of the method, all the local features are integrated together to be the basis for the identification. Experimental results on AR, YALE and ORL face databases show that the proposed method outperforms the 2DLPDA method.