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Open AccessJournal ArticleDOI

Robust Face Recognition via Sparse Representation

TLDR
This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Abstract
We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

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Journal ArticleDOI

Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

TL;DR: Experimental results show that the learning-based method proposed can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.
Journal ArticleDOI

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

TL;DR: A novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space is presented and a method to construct a sparse similarity graph, called L2-graph is introduced.
Journal ArticleDOI

NestFuse: An Infrared and Visible Image Fusion Architecture Based on Nest Connection and Spatial/Channel Attention Models

TL;DR: A novel method for infrared and visible image fusion where the nest connection-based network and spatial/channel attention models are developed that describe the importance of each spatial position and of each channel with deep features is proposed.
Posted Content

Understanding How Image Quality Affects Deep Neural Networks

TL;DR: In this paper, the authors provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions, including blur, noise, contrast, JPEG, and JPEG2000 compression.
Journal ArticleDOI

Spectral–Spatial Classification of Hyperspectral Images With a Superpixel-Based Discriminative Sparse Model

TL;DR: Experimental results on four real HSI datasets demonstrate the superiority of the proposed SBDSM algorithm over several well-known classification approaches in terms of both classification accuracies and computational speed.
References
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Regression Shrinkage and Selection via the Lasso

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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Convex Optimization

TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Journal ArticleDOI

Eigenfaces for recognition

TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI

Eigenfaces vs. Fisherfaces: recognition using class specific linear projection

TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
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What is the minimum number of images required for a facial recognition model to sufficiently learn features?

The paper does not provide a specific minimum number of images required for a facial recognition model to sufficiently learn features.