A
Arvind Ganesh
Researcher at University of Illinois at Urbana–Champaign
Publications - 107
Citations - 17189
Arvind Ganesh is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 24, co-authored 29 publications receiving 16148 citations. Previous affiliations of Arvind Ganesh include Urbana University.
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Robust Face Recognition via Sparse Representation
TL;DR: 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.
Proceedings Article
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization
TL;DR: It is proved that most matrices A can be efficiently and exactly recovered from most error sign-and-support patterns by solving a simple convex program, for which it is given a fast and provably convergent algorithm.
Journal ArticleDOI
RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images
TL;DR: This paper reduces this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of l1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques.
Journal ArticleDOI
Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation
TL;DR: This work proposes a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion, and demonstrates how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination.
Posted Content
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices
TL;DR: This paper has been withdrawn due to a critical error near equation (71).