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.read more
Citations
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Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders.
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Large Age-Gap face verification by feature injection in deep networks
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Marginal Representation Learning With Graph Structure Self-Adaptation
TL;DR: A marginally structured representation learning (MSRL) method is proposed by seamlessly incorporating distinguishable regression targets analysis, graph structure adaptation, and robust linear structural learning into a joint framework to demonstrate the efficacy of the proposed representation learning method in comparison with state-of-the-art algorithms.
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Automatic Subspace Learning via Principal Coefficients Embedding
TL;DR: These two challenging problems in unsupervised subspace learning can be simultaneously solved by proposing a new method called principal coefficients embedding (PCE), which can automatically determine the feature dimension of the learned subspace and is robust to the non-Gaussian noise.
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A Bayesian Nonparametric Approach to Image Super-Resolution
TL;DR: In this paper, the authors developed a new Bayesian nonparametric model for super-resolution which uses a beta-Bernoulli process to learn a set of recurring visual patterns, called dictionary elements, from the data.
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