Name
Title | ||
|---|---|---|
Face recognition using fisher non-negative matrix factorization with sparseness constraints |
Abstract | ||
|---|---|---|
A novel subspace method is proposed for part-based face recognition by using non-negative matrix factorization with sparseness constraints (NMFs) and Fisher's linear discriminant (FLD) hence its abbreviation, FNMFs. A comparative analysis engages PCA+FLD (FPCA) method and FNMFs method for both part-based and holistic-based face recognition. The comparative experiments are completed for the ORL face database and UMIST face database, it shows that FNMFs has better performance than FPCA-based method both for holistic-face and parts-face images recognition. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11427445_19 | ISNN (2) |
Keywords | Field | DocType |
part-based face recognition,parts-face images recognition,comparative experiment,fnmfs method,sparseness constraint,orl face database,fisher non-negative matrix factorization,novel subspace method,comparative analysis,holistic-based face recognition,umist face database,fpca-based method,face recognition,non negative matrix factorization,image recognition | Facial recognition system,Vector space,Pattern recognition,Subspace topology,Computer science,Matrix decomposition,Speech recognition,Artificial intelligence,Non-negative matrix factorization,Linear discriminant analysis,Machine learning | Conference |
Volume | ISSN | ISBN |
3497 | 0302-9743 | 3-540-25913-9 |
Citations | PageRank | References |
9 | 0.71 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaorong Pu | 1 | 85 | 11.17 |
| Zhang Yi | 2 | 1765 | 194.41 |
| Ziming Zheng | 3 | 272 | 13.57 |
| Wei Zhou | 4 | 238 | 40.27 |
| Mao Ye | 5 | 442 | 48.46 |