Name
Abstract | ||
|---|---|---|
Face recognition is the identification of humans by the unique characteristics of their faces and forms the basis for many biometric systems. In this research the problem of feature-based face recognition is considered. Bidimensional regression (BDR) is an extension of standard regression to 2D variables. Bidimensional regression can be used to determine the degree of resemblance between two planar configurations of points and for assessing the nature of their geometry. A primary advantage of this approach is that no training is needed. The goal of this research is to explore the suitability of BDR for 2D matching. Specifically, we explore if bidimensional regression can be used as a basis for a similarity measure to compare faces. The approach is tested using standard datasets. The results show that BDR can be effective in recognizing faces and hence can be used as an effective 2D matching technique. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s00138-008-0158-7 | Mach. Vis. Appl. |
Keywords | DocType | Volume |
Face recognition,Landmarks,Bidimensional regression | Journal | 21 |
Issue | ISSN | Citations |
3 | 0932-8092 | 7 |
PageRank | References | Authors |
0.55 | 25 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarvani Kare | 1 | 7 | 0.55 |
| A Samal | 2 | 1033 | 213.54 |
| David Marx | 3 | 79 | 5.70 |