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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Journal ArticleDOI
Efficient Superpixel-Level Multitask Joint Sparse Representation for Hyperspectral Image Classification
TL;DR: A superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery that exploits the class-level sparsity prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region is proposed.
Journal ArticleDOI
Multidimensional Scaling for Matching Low-Resolution Face Images
TL;DR: The proposed approach for matching low-resolution probe images with higher resolution gallery images, which are often available during enrollment, using Multidimensional Scaling (MDS), improves the matching performance significantly as compared to performing matching in the low- resolution domain or using super-resolution techniques to obtain a higher resolution test image prior to recognition.
Journal ArticleDOI
A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data
TL;DR: A unified framework that makes the representation-based subspace clustering algorithms feasible to cluster both the out-of-sample and the large-scale data, and gives an estimation for the error bounds by treating each subspace as a point in a hyperspace.
Journal ArticleDOI
Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition
TL;DR: Comparisons with competing methods on benchmark real-world databases consistently show the superiority of the proposed methods for both color FR and reconstruction.
Journal ArticleDOI
Structure-constrained low-rank representation.
TL;DR: It is proved that the relationship of multiple linear disjoint subspaces can be exactly revealed by SC-LRR, with a predefined weight matrix, and illustrated that SC- LRR can be applied for semisupervised learning.
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