Face Verification via ECOC
Josef Kittler, Reza Ghaderi, Terry Windeatt, Jiřı́ Matas · 2001
We develop a novel approach to face 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 (superclasses) 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. The proposed method exhibits superior verification performance on the well known XM2VTS data set as compared with previously reported results. 1