基于二维邻域保持判别嵌入的人脸识别

Face Recognition Based on Two-Dimensional Neighborhood Preserving Discriminant Embedding

提出二维邻域保持判别嵌入(2DNPDE)算法,该算法是一种有监督的基于二维图像矩阵的特征提取算法.为表示样本的类内邻域结构和类间距离关系,分别构建类内邻接矩阵和类间相似度矩阵.2DNPDE所获得的投影空间不但使不同类数据点的低维嵌入相互分离,而且保留同类样本的邻域结构和不同类样本的距离关系.在ORL和AR人脸数据库上的实验表明,该算法具有更好的识别效果.

Two-dimensional neighborhood preserving discriminant embedding （ 2 DNPDE ） is proposed in this paper. 2DNPDE is supervised feature extraction algorithm based on 2D image matrices. For representing the within-class neighborhood structure and the between-class distance relationship of samples, the within-class affinity matrix and the between-class similarity matrix are constructed respectively. The projection space obtained by 2 DNPDE not only makes the low dimensional embedding of data points from different classes far from each other, but also preserves the neighborhood structure of samples from the same class and the distance relationship of samples from the different classes. The experimental results on the ORL and AR face databases show that the proposed algorithm has better recognition performance.