一种快速非负矩阵分解的人脸识别算法

用于人脸识别的非负矩阵分解算法,虽可提高图像识别率,但因其是通过迭代方法同时计算出基矩阵和系数矩阵,故当迭代次数较多时,计算过程耗时长。文中将二维线性判别分析方法与非负矩阵分解方法融合,提出了一种快速的双边二维非负矩阵分解算法。通过在AR、Ｙale人脸数据库上的实验结果显示,较二维双边非负矩阵分解算法,文中算法不仅使得训练时间大幅减少,而且识别率也有所提高。

A Rapid Non-negative Matrix Decomposition Method for Face Recognition

Face recognition algorithms through non-negative matrix factorization can increase image recognition rat,but it is an iterative algorithm which must simultaneously calculates the base matrix and the coefficient matrix,leading to high computational complexity with many iterations.This paper introduces the 2-dimensional linear discriminant analysis into the non-negative matrix factorization algorithm,and proposes a fast two-dimensional bilateral non-negative matrix factorization algorithm.Experiment results on the AR and the Yale face database show that the proposed algorithm has higher recognition performance as well as a much faster speed than the two-dimensional bilateral non-negative matrix factorization algorithm.