Name
Abstract | ||
|---|---|---|
We consider learning a discriminative dictionary in sparse representation and specifically focus on face recognition application to improve its performance. This paper presents an algorithm to learn a discriminative dictionary with low-rank regularization on the dictionary. To make the dictionary more discerning, we apply Fisher discriminant function to the coding coefficients with the goal that they have a small ratio of the within-class scatter to between-class scatter. However, noise in the training samples will undermine the discrimination power of the dictionary. To handle this problem, we base on low-rank matrix recovery theory and apply a low-rank regularization on the dictionary. The proposed discriminative dictionary learning with low-rank regularization (D2L2R2) algorithm is evaluated on several face image datasets in comparison with existing representative dictionary learning and classification algorithms. The experimental results demonstrate its superiority. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FG.2013.6553696 | FG |
Keywords | Field | DocType |
image representation,face recognition,learning (artificial intelligence),discriminative dictionary learning,between-class scatter,face image datasets,fisher discriminant function,within-class scatter,low-rank regularization,sparse representation,databases,learning artificial intelligence,noise,dictionaries,encoding,sparse matrices | Facial recognition system,Pattern recognition,K-SVD,Computer science,Sparse approximation,Speech recognition,Regularization (mathematics),Artificial intelligence,Linear discriminant analysis,Statistical classification,Discriminative model,Sparse matrix | Conference |
ISSN | ISBN | Citations |
2326-5396 | 978-1-4673-5544-5 | 18 |
PageRank | References | Authors |
0.65 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liangyue Li | 1 | 137 | 10.68 |
| Sheng Li | 2 | 609 | 53.39 |
| Yun Fu | 3 | 4267 | 208.09 |