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	<title>collaborative representation Archives &#8212; MATLAB Number ONE</title>
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	<description>MATLAB Simulink &#124; Tutorial &#124; Code &#124; Project</description>
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	<title>collaborative representation Archives &#8212; MATLAB Number ONE</title>
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		<title>Robust Kernel Representation with Statistical Local Features for Face Recognition</title>
		<link>https://matlab1.com/shop/matlab-code/robust-kernel-representation-with-statistical-local-features-for-face-recognition/</link>
					<comments>https://matlab1.com/shop/matlab-code/robust-kernel-representation-with-statistical-local-features-for-face-recognition/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Tue, 08 May 2018 05:12:19 +0000</pubDate>
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					<description><![CDATA[<p>In this project , we proposed a statistical local feature based robust kernel representation (SLF-RKR) model for face recognition. A robust representation model to image outliers (e.g., occlusion and real disguise) was built in the kernel space, and a multi-partition max pooling technology was proposed to enhance the invariance of local pattern feature to image misalignment and pose [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/robust-kernel-representation-with-statistical-local-features-for-face-recognition/">Robust Kernel Representation with Statistical Local Features for Face Recognition</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Sparse or Collaborative Representation for Face Recognition</title>
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					<comments>https://matlab1.com/shop/matlab-code/sparse-or-collaborative-representation-for-face-recognition/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Tue, 08 May 2018 03:35:13 +0000</pubDate>
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					<description><![CDATA[<p>As a recently proposed technique, sparse representation 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 minimum representation error. While the importance of sparsity is much emphasized in SRC and many [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/sparse-or-collaborative-representation-for-face-recognition/">Sparse or Collaborative Representation for Face Recognition</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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