Name
Abstract | ||
|---|---|---|
This paper studies how biologically meaningful landmarks extracted from face images can be exploited for face recognition using the bidimensional regression. Incorporating the correlation statistics of landmarks, this paper also proposes a new approach called eigenvalue weighted bidimensional regression. Complex principal component analysis is used for computing eigenvalues and removing correlation among landmarks. We evaluate our approach using two standard face databases: the Purdue AR and the NIST FERET. Experimental results show that the bidimensional regression is an efficient method to exploit geometry information of face images. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICDM.2005.61 | ICDM |
Keywords | Field | DocType |
correlation statistic,face recognition,face image,regression analysis,bidimensional regression,purdue ar,standard face databases,paper study,biologically meaningful landmarks,landmark-based bidimensional regression,eigenvalue weighted bidimensional regression,correlation statistics,new approach,face images,eigenvalues and eigenfunctions,nist feret,principal component analysis,eigenvalues | Facial recognition system,Regression,Pattern recognition,Regression analysis,Computer science,NIST,Correlation,Artificial intelligence,Landmark,Principal component analysis,Eigenvalues and eigenvectors,Machine learning | Conference |
ISSN | ISBN | Citations |
1550-4786 | 0-7695-2278-5 | 7 |
PageRank | References | Authors |
0.68 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiazheng Shi | 1 | 57 | 7.63 |
| A Samal | 2 | 1033 | 213.54 |
| David Marx | 3 | 79 | 5.70 |