representación dispersa Archives — MATLAB Number ONE

Regularized Robust Coding for Face Recogntion

global MATLAB — Tue, 08 May 2018 05:25:07 +0000

This paper presented a novel robust regularized coding (RRC) model and an associated effective iteratively reweighted regularized robust coding (IR3 C) algorithm for robust face recognition (FR). One important advantage of RRC is its robustness to various types of outliers (e.g., occlusion, corruption, expression, etc.) by seeking for an approximate MAP (maximum a posterior estimation) solution of the coding […]

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Robust Kernel Representation with Statistical Local Features for Face Recognition

global MATLAB — Tue, 08 May 2018 05:12:19 +0000

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 […]

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Metasample Based Sparse Representation for Tumor Classification

global MATLAB — Mon, 07 May 2018 05:13:21 +0000

A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l1 -norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR based method […]

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On the Dimensionality Reduction for Sparse Representation based Face Recognition

global MATLAB — Mon, 16 Apr 2018 03:59:10 +0000

In this project, we discussed the dimensionality reduction (DR) of face images when using sparse representation based classifier (SRC) for classification. Our experiments on Extended Yale B, AR and ORL face databases demonstrated that the proposed DR algorithm has better performance than Eigenfaces and Randomfaces. It can achieve higher recognition rate under the same dimensionality […]

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