基于分块非负矩阵分解人脸识别增量学习  

Incremental learning of face recognition based on block non-negative matrix factorization

作　　者：潘彬彬[1] 陈文胜[1] 徐晨[1] 

机构地区：[1]深圳大学数学与计算科学学院,智能计算科学研究所,广东深圳518060

出　　处：《计算机应用研究》2009年第1期117-120,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(60873168);广东省自然科学基金资助项目(06105776)

摘　　要：非负矩阵分解(NMF)算法可以提取图像的局部特征,然而NMF算法有两个主要缺点:a)当矩阵维数较大时,NMF算法非常耗时;b)当增加新的训练样本或类别时,NMF算法必须进行重复学习。为克服NMF算法这些缺点,提出了一种新的分块NMF算法(BNMF)。特别地,该方法还可用于增量学习。通过在FERET和CMUPIE人脸数据库上进行实验,结果表明该算法均优于NMF和PCA算法。Non-negative matrix factorization （NMF） can extract local features of images. However, NMF method has two main drawbacks. One shortcoming is that it is very time-consuming to deal with large matrices. The other is that it must implement repetitive learning, when the training samples or classes are incremental. In order to overcome these two limitations, this paper presented a novel block NMF （BNMF） method. In particular, it could be applied to incremental learning. Two face databases, namely FERET and CMU PIE face databases, were selected for evaluation. Comparing with NMF and PCA schemes, the proposed method gives superior results.

关 键 词：非负矩阵分解 局部特征提取 人脸识别 增量学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]