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ma yi sparse representation classification .EXTENDED YALE B database.recognition rate 95 。-ma yi sparse representation classification. recognition rate 95 .
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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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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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基于类别相关近邻子空间的最大似然稀疏表示鲁棒图像识别算法-Category neighboring subspace-based maximum likelihood sparse representation robust image recognition algorithm
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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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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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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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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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信号稀疏表示是一种新兴的信号分析和综合方法，其目的就是在过完备字典中用尽可能少的原子来表示信号。采用时频原子字典的信号稀疏表示能够有效地揭示非平稳信号的时变特征。信号稀疏表示吸引了研究者的大量关注，这种方法已经被应用到信号处理的许多方面，例如非平稳信号分析，信号编码、识别与信号去噪等。-Signal sparse representation is a new method of signal analysis and synthesis, and its purpose is in over-
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使用小波框架并结合稀疏表示，用于SAR图像的目标识别应用(Application of wavelet frame and sparse representation for target recognition in SAR images)
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