Name
Abstract | ||
|---|---|---|
Even though numerous techniques for face recognition have been explored over the years, most research has primarily focussed on identification from full frontal/profile facial images. This paper conducts a first systemic study to assess the performance when using partial-faces for identification. Our specific approach considers an ensemble of Radial Basis Function (RBF) Networks. A specific advantage of using an ensemble is its ability to cope with the inherent variability in the image formation and data acquisition process. Our database consists of imagery corresponding to 150 unique subjects totaling to 3,000 facial images with +/- 5 degree rotation. Based on our experimental results, we observe that the average Cross Validation performance is the same even if only half the face image is used instead of the full-face image. Specifically we obtain 96% when partial-faces are used and 97% when full-faces are used. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/AFGR.2002.1004126 | Washington, DC |
Keywords | Field | DocType |
face image,specific approach,face recognition,average cross validation performance,radial basis function,profile facial image,specific advantage,facial image,image formation,full-face image,data acquisition,cross validation,rotation | Facial recognition system,Computer vision,Radial basis function,Pattern recognition,Computer science,Data acquisition,Image formation,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
0-7695-1602-5 | 21 | 1.35 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srinivas Gutta | 1 | 162 | 22.03 |
| Vasanth Philomin | 2 | 409 | 93.18 |
| Miroslav Trajković | 3 | 126 | 9.15 |