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这是一种基于稀释的人脸识别算法。这个算法能够有效的处理脸部遮挡问题。-Robust Sparse Coding for Face Recognition
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判别稀疏非负矩阵分解，提出这个新算法，来进行人脸识别，比传统的NMF和一些其他的扩展算法效果好-Sparse non-negative matrix factorization judge proposed the new algorithm for face recognition, than the traditional extension of NMF algorithm and some other good results
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目前比较流行的稀疏分解重构程序，可以用在人脸识别、字典构造等方面。-Currently popular sparse decomposition and reconstruction process, can be used in face recognition, dictionary structure and so on.
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Sparse Representation or Collaborative Representation Which Helps Face Recognition
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This my course final project for biometrics. It implements a robust face recognition system. Techniques used include Log-Gabor filter , Kernel Discriminant Analysis(KDA) and Sparse Representation . Test data included(ORL database of faces).-This is m
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illumination-robust face recognition via sparse representation
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文獻「Robust Sparse Coding for Face Recognition」
及matlab代碼-「Robust Sparse Coding for Face Recognition」 and matlab source code.
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參考文獻「Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary」and matlab code.-「Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary」and matlab code.
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稀疏表示人脸分类与识别，Mayi人脸分类识别框架，识别率非常高-sparse represention for face recognition
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基于稀疏表示的人脸识别算法，是核心算法的演示版本-Face recognition algorithm based on sparse representation is the demo version of the core algorithm
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based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the mini
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based classification (SRC) has been widely used for face
recognition (FR). SRC first codes a testing sample as a
sparse linear combination of all the training samples, and
then classifies the testing sample by evaluating which class
leads to
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本matlab程序适用于论文Robust Sparse Coding for Face Recognition
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Face recognition using L1 norm minimization 1.0 :Read the following paper for details of the algorithm - Robust Face Recognition via Sparse Representation by John Wright, Arvind Ganesh, and Yi Ma , Coordinated Science Laboratory, University of Illino
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基于稀疏表达的包含测试和训练人脸的数据库的鲁棒性人脸识别-Sparse expression includes testing and training human face recognition database robustness
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稀疏人脸识别,来至论文Robust Face Recognition via Sparse Representation-Robust Face Recognition via Sparse Representation
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Face recognition via Weighted Sparse Representation Face recognition via Weighted Sparse Representation
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2015数学建模B题，包括了人工智能很多知识，有谱聚类，多流形学习，人脸识别，以及稀疏子空间聚类。其中的英文参考文献很有价值。-2015 mathematical modeling of B problems, including the artificial intelligence of a lot of knowledge, a spectral clustering, manifold learning, face recognition, as well as sparse subsp
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源码实现了使用基于稀疏表示的人脸识别算法。使用GPSR作为l1模最小化方法。-Source code to achieve the use of sparse representation based on the face recognition algorithm. Using GPSR as a method for minimizing the L1 norm.
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这是关于稀疏字典学习用于人脸识别图像处理的文章，里面是对应的算法代码-This is an article about sparse dictionary learning for face recognition image processing, which is the corresponding algorithm code
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