基于核非负稀疏表示的人脸识别

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基于核非负稀疏表示的人脸识别

作者：薄纯娟张汝波刘冠群汪语哲

来源：《计算机应用》2014年第08期

摘要：人脸识别是计算机视觉和模式识别领域的非常重要研究课题之一。基于此课题提出了一种新颖的核非负稀疏表示（KNSR）算法，并将其在人脸库上进行测试将其用于人脸识别，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。

关键词：人脸识别；稀疏表示；核函数；局部二元特征；汉明核

中图分类号： TP391.413

文献标志码：A

Abstract： Face recognition is one of important topics in computer vision and pattern recognition.

A novel kernelbased nonnegative sparse representation （KNSR） method was presented based on this topic and was tested on face databasesfor face recognition. The contributions were mainly three aspects： First， the nonnegative constraints on representation coefficients were introduced into the Sparse Representation （SR） and the kernel function was exploited to depict nonlinear 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 nonlinear 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 Yale

B and AR databases.

Key words： face recognition； sparse representation； kernel function； local binary feature；Hamming kernel

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