Name
Abstract | ||
|---|---|---|
We propose a novel approach to face identification and verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase the client set is repeatedly divided into two ECOC specified sub-sets (super-classes) to train a set of binary classifiers. The output of the classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns representing clients and impostors. As a matching score in this space we propose the average first order Minkowski distance between the probe and gallery images. The proposed method exhibits superior verification performance on the well known XM2VTS data set as compared with previously reported results. |
Year | Venue | Keywords |
|---|---|---|
2001 | AVBPA | binary classifier,client set,superior verification performance,ecoc specified sub-sets,matching score,xm2vts data,error correcting output coding,gallery image,ecoc feature space,face identification,classifier design concept,first order,feature space,personal identity |
Field | DocType | Volume |
Minkowski distance,Computer science,Image processing,Artificial intelligence,Code word,Classifier (linguistics),Distributed computing,Feature vector,Pattern recognition,Error detection and correction,Linear discriminant analysis,Biometrics,Machine learning | Conference | 2091 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-42216-1 | 3 |
PageRank | References | Authors |
0.69 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Kittler | 1 | 14346 | 1465.03 |
| Reza Ghaderi | 2 | 199 | 14.97 |
| Terry Windeatt | 3 | 501 | 44.59 |
| Jiri Matas | 4 | 335 | 35.85 |