Name
Abstract | ||
|---|---|---|
The locality preserving projection (LPP), known as Laplacianfaces, was recently proposed as a transformation technique of mapping which optimally preserves the neighborhood structure of the dataset. In this paper, an efficient method for face recognition called mixture-of-Laplacianfaces (or LPP mixture model) is proposed, which obtains several sets of Laplacianfaces through Expectation-Maximization (EM) learning of Gaussian Mixture Models (GMM). Experiments carried out by using this on ORL, FERET and COIL-20 indicate superior performance as compared with method based on Laplacianfaces and other contemporary subspace methods. |
Year | Venue | Keywords |
|---|---|---|
2007 | PReMI | face recognition,gaussian mixture models,neighborhood structure,efficient method,lpp mixture model,transformation technique,contemporary subspace method,superior performance,expectation maximization,computer science,mixture model,gaussian mixture model |
Field | DocType | Volume |
Facial recognition system,Locality,Subspace topology,Pattern recognition,FERET,Computer science,Artificial intelligence,Mixture model,Machine learning,Laplace operator | Conference | 4815 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-77045-3 | 2 |
PageRank | References | Authors |
0.41 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Noushath | 1 | 109 | 7.27 |
| Ashok Rao | 2 | 200 | 19.14 |
| G. Hemantha Kumar | 3 | 222 | 27.92 |