Locality-constrained group sparse representation for robust face recognition
Locality-constrained group sparse representation for robust face recognition
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DOI：
10.1109/icip.2011.6116666
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发表时间：
2011-12
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通讯作者：
Yu-Wei Chao;Yi-Ren Yeh;Yu-Wen Chen;Yuh-Jye Lee;Y. Wang
中科院分区：
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作者：
Yu-Wei Chao;Yi-Ren Yeh;Yu-Wen Chen;Yuh-Jye Lee;Y. Wang
提出了一种新的用于鲁棒人脸识别的稀疏表示方法。我们提出了组稀疏性和数据局部性，并制定了统一的优化框架，该框架产生了局部性和组敏感稀疏表示（LGSR），以提高识别。实证结果证实，我们的LGSR不仅优于最先进的基于稀疏编码的图像分类方法，而且我们的方法对光照、姿势和面部细节（眼镜与否）等变化具有鲁棒性，这些变化在现实世界的人脸识别问题中很常见。
This paper presents a novel sparse representation for robust face recognition. We advance both group sparsity and data locality and formulate a unified optimization framework, which produces a locality and group sensitive sparse representation (LGSR) for improved recognition. Empirical results confirm that our LGSR not only outperforms state-of-the-art sparse coding based image classification methods, our approach is robust to variations such as lighting, pose, and facial details (glasses or not), which are typically seen in real-world face recognition problems.