基于核非负稀疏表示的人脸识别  

Face recognition via kernel-based non-negative sparse representation

作　　者：薄纯娟[1] 张汝波[1] 刘冠群[1] 汪语哲[1] 

机构地区：[1]大连民族学院机电信息工程学院,辽宁大连116600

出　　处：《计算机应用》2014年第8期2227-2230,共4页journal of Computer Applications

基　　金：国家863计划项目(2009AA04Z215);中央高校基础科研基金资助项目(DC13010305)

摘　　要：提出了一种新颖的核非负稀疏表示(KNSR)算法,将其用于人脸识别,主要贡献有如下3个方面:首先,在稀疏表示(SR)的基础上引入了对表示系数的非负限制,并利用核函数来描述样本之间的非线性关系,提出了相应的目标函数;其次,提出了一种乘性梯度下降迭代算法对提出的目标函数进行优化求解,该算法在理论上可以保证收敛到全局最优值;最后,利用局部二元特征和汉明核来建模人脸样本的非线性关系,从而实现鲁棒的人脸识别。实验结果表明,在具有挑战性的人脸库上所提算法识别率均高于最近邻(NN)算法、支持向量机(SVM)、最近子空间(NS)、SR和协同表示(CR)算法,在YaleB和AR数据库上都达到了大约99%的识别率。A novel kernel-based non-negative sparse representation （KNSR） method was presented for face recognition. The contributions were mainly three aspects： First, the non-negative constraints on representation coefficients were introduced into the Sparse Representation （SR） and the kernel function was exploited to depict non-linear relationships among different samples, based on which the corresponding objective function was proposed. Second, a multiplicative gradient descent method was proposed to solve the proposed objective function, which could achieve the global optimum value in theory. Finally, local binary feature and the Hamming kernel were used to model the non-linear relationships among face samples and therefore achieved robust face recognition. The experimental results on some challenging face databases demonstrate that the proposed algorithm has higher recognition rates in comparison with algorithms of Nearest Neighbor （NN）, Support Vector Machine （SVM）, Nearest Subspace （NS）, SR and Collaborative Representation （CR）, and achieves about 99% recognition rates on both YaleB and AR databases.

关 键 词：人脸识别 稀疏表示 核函数 局部二元特征 汉明核 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]