Robust Face Recognition via Sparse Representation
Citations
3,085 citations
Cites background from "Robust Face Recognition via Sparse ..."
...near) subspaces are possibly the most common choice, mainly because they are easy to compute and often effective in real applications. Several types of visual data, such as motion [1], [2], [3], face [4] and texture [5], have been known to be well characterized by subspaces. Moreover, by applying the concept of reproducing kernel Hilbert space [6], one can easily extend the linear models to handle no...
[...]
...) data into clusters with each cluster corresponding to a subspace. Subspace segmentation is an important data clustering problem and arises in numerous research areas, including computer vision [3], [4], [10], [11], image processing [5], [12], [13], machine learning [14], [15] and system identification [16]. When the data is clean, i.e., the samples are strictly drawn from the subspaces, several exis...
[...]
2,784 citations
Cites methods from "Robust Face Recognition via Sparse ..."
...ly few incoherent random measurements [12, 8, 9, 7, 2, 43]. The sparse solution ˘ to Eq. 1 may also be used for sparse classification schemes, such as the sparse representation for classification (SRC) [44]. Importantly, the compressive sensing and sparse representation architectures are convex and scale well to large problems, as opposed to brute-force combinatorial alternatives. 3 3 Sparse identificati...
[...]
2,298 citations
2,001 citations
Cites background from "Robust Face Recognition via Sparse ..."
...In [8], Wright et al. reported a very interesting work by using sparse representation for robust face recognition (FR)....
[...]
1,871 citations
Cites methods from "Robust Face Recognition via Sparse ..."
...The answer has been largely positive: in the past few years, variations and extensions of `1 minimization have been applied to many vision tasks, including ∗John Wright and Yi Ma are with the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign....
[...]
References
40,785 citations
Additional excerpts
...Ç...
[...]
40,147 citations
"Robust Face Recognition via Sparse ..." refers background in this paper
...What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed....
[...]
[...]
33,341 citations
14,562 citations
"Robust Face Recognition via Sparse ..." refers background in this paper
...…dictionary of base elements or signal atoms has seen a recent surge of interest [9], [10], [11], [12].1 Much of this excitement centers around the discovery that whenever the optimal representation is sufficiently sparse, it can be efficiently computed by convex optimization [9], even though…...
[...]
11,674 citations
"Robust Face Recognition via Sparse ..." refers background or methods in this paper
...One class of methods extracts holistic face features such as Eigenfaces[23], Fisherfaces [ 24 ], and Laplacianfaces [25]....
[...]
...high-dimensional test image into lower dimensional feature spaces: examples include Eigenfaces [23], Fisherfaces [ 24 ], Laplacianfaces [25], and a host of variants [26], [27]....
[...]
...the other features because the maximal number of valid Fisherfaces is one less than the number of classes k [ 24 ], 38 in this case....
[...]
...Subspace models are flexible enough to capture much of the variation in real data sets and are especially well motivated in the context of face recognition, where it has been observed that the images of faces under varying lighting and expression lie on a special low-dimensional subspace [ 24 ], [30], often called a face subspace....
[...]