基于改进2D-DLPP算法的人脸识别

在二维局部保持投影中引入类间结构信息和类标签,得到有监督的二维判别局部保持投影算法,从而提高了特征集的鉴别性。针对算法中参数的选取问题,建立无参数权重矩阵,提出无参数的二维判别局部保持投影(无参数2D-DLPP)算法。在Yale和ORL人脸库上的仿真实验结果表明,该算法与二维判别局部保持投影(2D-DLPP)、二维局部保持投影法(2D-LPP)和二维线性判别分析法(2D-LDA)相比能够取得更高的识别率。

Face Recognition Based on Improved 2D-DLPP Algorithm

By introducing between- class scatter constraint and label information into two- dimensional locality preserving projections（ 2D- LPP） algorithm, two- dimensional discriminant locality preserving projections（2D-DLPP） has more discriminant power than 2D-LPP. However, 2D-DLPP is confronted with the difficulty of parameter selection, which limits its power on solving recognition problem. To solve this problem, by constructing parameter-less affinity matrix, an algorithm called parameter-less two-dimensional discriminant locality preserving projections（parameter-less 2D-DLPP） is proposed. The simulation results on Yale and ORL face database show that the method in this paper can get higher recognition rate than2D-DLPP, 2D-LPP and 2D-LDA.