Name
Abstract | ||
|---|---|---|
We address the problem of face verification using linear discriminant anal- ysis and investigate the issue of matching score1. We establish the reason behind the success of the normalised correlation. The improved understand- ing about the role of metric then naturally leads to a novel way of measuring the distance between a probe image and a model. In extensive experimen- tal studies on the publicly available XM2VTS database2 using the Lausanne protocol3 we show that the proposed metric is consistently superior to both the Euclidean distance and normalised correlation matching scores. The ef- fect of various photometric normalisations4 on the matching scores is also investigated. |
Year | Venue | Keywords |
|---|---|---|
2000 | BMVC | euclidean distance |
Field | DocType | Citations |
Face verification,Pattern recognition,Computer science,Euclidean distance,Correlation,Artificial intelligence,Correlation matching,Linear discriminant analysis | Conference | 49 |
PageRank | References | Authors |
4.63 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Kittler | 1 | 14346 | 1465.03 |
| Yongping Li | 2 | 175 | 18.25 |
| Jiri Matas | 3 | 335 | 35.85 |