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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Latent Space Sparse Subspace Clustering
TL;DR: A method that learns the projection of data and finds the sparse coefficients in the low-dimensional latent space and applies spectral clustering to a similarity matrix built from these sparse coefficients.
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Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking
TL;DR: The proposed joint sparse representation model dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation and is extended into a general kernelized framework, which is able to perform feature fusion on various kernel spaces.
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Recent advances in feature selection and its applications
TL;DR: This review paper presents a selection of challenges which are of particular current interests, such as feature selection for high-dimensional small sample size data, large-scale data, and secure feature selection, as well as some representative applications of feature selection.
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Relaxed collaborative representation for pattern classification
TL;DR: A novel relaxed collaborative representation (RCR) model to effectively exploit the similarity and distinctiveness of features and is very competitive with state-of-the-art image classification methods.
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Multi-label sparse coding for automatic image annotation
TL;DR: A label sparse coding based subspace learning algorithm is derived to effectively harness multi-label information for dimensionality reduction and propagate the multi-labels of the training images to the query image with the sparse l1 reconstruction coefficients.
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