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本文应用SMQT和 SPLIT UP SNOW 分类器来完成对人脸的检测。-The purpose of this paper is threefold: firstly, the local Successive
Mean Quantization Transform features are proposed for illumination
and sensor insensitive operation in object recognition. Secondly, a
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用于脸部识别时，使用基于像素和区域的图像特征核的稀疏表示的识别算法。-When used in face recognition using image recognition algorithm based on feature pixel and regional nuclear 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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单样本人脸识别中，将训练样本分块，以增加训练样本的数量，构成字典基，用稀疏表示的方法求解人脸识别问题-Single sample of face recognition, the training sample block, in order to increase the number of training samples, a dictionary base, with sparse representation method to solve the problem of face re
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MATLAB编写的基于稀疏表达的人脸识别技术，内含一个简单便于理解的例子-MATLAB prepared based on face recognition technology sparse expression, there is a need to see! !
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Lei Zhang, Meng Yang, and Xiangchu Feng,
Sparse Representation or Collaborative Representation: Which Helps Face Recognition? in ICCV 2011 源代码-Lei Zhang, Meng Yang, and Xiangchu Feng,
Sparse Representation or Collaborative Representation: Whic
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稀疏矩阵，人脸识别，图像处理，指纹识别,图形识别-Sparse matrix face recognition image processing fingerprint identification
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图像处理，稀疏表示，字典去噪，ksvd，人脸识别-Image processing, sparse representation, dictionary denoising, KSVD, face recognition
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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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以稀疏子空间聚类以及低秩子空间聚类等基本谱聚类算法为基础，通过
运用核映射算法，融合与数据本身结构相关的局部切线空间函数以及主成分分析
算法建立了可以应对独立子空间聚类、非独立子空间聚类、非线性聚类、混合多
流体聚类问题以及多种含有大数据量的实际问题，包括处理运动分割、人脸识别、
工件识别等情况中的多种类型数据分类的聚类算法，并且引入 Map-Reduce 并行处
理方法优化了算法的计算效率(Based on the basic spectral clustering algorith
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使用稀疏特征算法实现人脸识别，带有例程，基本实现人脸识别(Using sparse feature algorithm to realize face recognition with routineBasic realization of face recognition)
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