基于平方损失的正则化LDA图像集人脸识别

Yanlin Geng, Caifeng Shan, Pengwei Hao
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引用次数: 5

摘要

在本文中,我们关注图像集上的人脸识别,其中每个集由一个线性子空间表示。判别学习采用线性判别分析(LDA)。在研究Fisher准则的正则化与最大余量准则的正则化关系的基础上,提出了正则化LDA的统一框架。利用该框架,正则化Fisher LDA的比值形式最大化问题可以简化为附加约束的差分形式优化问题。通过将经验损失作为正则化项,提出了一种基于广义平方损失的正则化LDA (SLR-LDA),并给出了参数设置建议。我们的方法在人脸识别方面取得了比最先进的方法更好的性能。在一般物体识别和物体类别识别实验中,也验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Square Loss based regularized LDA for face recognition using image sets
In this paper, we focus on face recognition over image sets, where each set is represented by a linear subspace. Linear Discriminant Analysis (LDA) is adopted for discriminative learning. After investigating the relation between regularization on Fisher Criterion and Maximum Margin Criterion, we present a unified framework for regularized LDA. With the framework, the ratio-form maximization of regularized Fisher LDA can be reduced to the difference-form optimization with an additional constraint. By incorporating the empirical loss as the regularization term, we introduce a generalized Square Loss based Regularized LDA (SLR-LDA) with suggestion on parameter setting. Our approach achieves superior performance to the state-of-the-art methods on face recognition. Its effectiveness is also evidently verified in general object and object category recognition experiments.
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