Robust Face Recognition via Sparse Representation
Robust Face Recognition via Sparse Representation
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DOI：
10.1109/tpami.2008.79
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发表时间：
2009-02-01
影响因子：
23.6
通讯作者：
Ma, Yi
中科院分区：
文献类型：
--
作者：
Wright, John;Yang, Allen Y.;Ma, Yi
我们考虑的问题，自动识别人脸的正面视图与不同的表情和照明，以及遮挡和伪装。我们把识别问题看作是多元线性回归模型中的一个分类问题，并认为稀疏信号表示的新理论提供了解决这个问题的关键。基于l（1）-最小化计算的稀疏表示，我们提出了一个通用的分类算法（基于图像）的目标识别。这个新框架为人脸识别中的两个关键问题提供了新的见解：特征提取和对遮挡的鲁棒性。对于特征提取，我们表明，如果稀疏性的识别问题得到妥善利用，功能的选择不再是关键。然而，关键是特征的数量是否足够大以及稀疏表示是否被正确计算。非常规特征，如下采样图像和随机投影，只要特征空间的维度超过稀疏表示理论预测的特定阈值，就可以与传统特征，如特征脸和拉普拉斯脸一样好地执行。该框架可以处理由于遮挡和腐败的错误，通过利用这些错误相对于标准（像素）的基础通常是稀疏的这一事实。稀疏表示理论有助于预测识别算法可以处理多大的遮挡，以及如何选择训练图像以最大限度地提高对遮挡的鲁棒性。我们在公开的数据库上进行了大量的实验，以验证所提出的算法的有效性，并证实了上述说法。
We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by l(1)-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as Eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.