Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Sparse Representation Based Classification for Face
  Recognition with Insufficient Labeled Samples   This paper addresses the problem of face recognition when there is only few,
or even only a single, labeled examples of the face that we wish to recognize.
Moreover, these examples are typically corrupted by nuisance variables, both
linear (i.e., additive nuisance variables such as bad lighting, wearing of
glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables such
as expression changes). The small number of labeled examples means that it is
hard to remove these nuisance variables between the training and testing faces
to obtain good recognition performance. To address the problem we propose a
method called Semi-Supervised Sparse Representation based Classification
(S$^3$RC). This is based on recent work on sparsity where faces are represented
in terms of two dictionaries: a gallery dictionary consisting of one or more
examples of each person, and a variation dictionary representing linear
nuisance variables (e.g., different lighting conditions, different glasses).
The main idea is that (i) we use the variation dictionary to characterize the
linear nuisance variables via the sparsity framework, then (ii) prototype face
images are estimated as a gallery dictionary via a Gaussian Mixture Model
(GMM), with mixed labeled and unlabeled samples in a semi-supervised manner, to
deal with the non-linear nuisance variations between labeled and unlabeled
samples. We have done experiments with insufficient labeled samples, even when
there is only a single labeled sample per person. Our results on the AR,
Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is
able to deliver significantly improved performance over existing methods.

30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys