Paper Info
Title
Sparse Representation Based Face Recognition with Limited Labeled Samples
Abstract
Sparse representations have emerged as a powerful approach for encoding images in a large class of machine recognition problems including face recognition. These methods rely on the use of an over-complete basis set for representing an image. This often assumes the availability of a large number of labeled training images, especially for high dimensional data. In many practical problems, the number of labeled training samples are very limited leading to significant degradations in classification performance. To address the problem of lack of training samples, we propose a semi-supervised algorithm that labels the unlabeled samples through a multi-stage label propagation combined with sparse representation. In this representation, each image is decomposed as a linear combination of its nearest basis images, which has the advantage of both locality and sparsity. Extensive experiments on publicly available face databases show that the results are significantly better compared to state-of-the-art face recognition methods in semi-supervised setting and are on par with fully supervised techniques.
Year
DOI
Venue
2013
10.1109/ACPR.2013.38
ACPR
Keywords
Field
DocType
large class,face recognition,available face databases,encoding image,limited labeled samples,state-of-the-art face recognition method,large number,training image,sparse representation,machine recognition problem,training sample,compressed sensing,image classification,sparse matrices,semi supervised learning
Facial recognition system,Clustering high-dimensional data,Semi-supervised learning,Pattern recognition,Computer science,Sparse approximation,Artificial intelligence,Contextual image classification,Compressed sensing,Sparse matrix,Machine learning,Encoding (memory)
Conference
Citations 
PageRank 
References 
1
0.35
11
Authors
3
Name
Order
Citations
PageRank
Vijay Kumar1363.83
Anoop M. Namboodiri225526.36
C. V. Jawahar31700148.58