We propose a novel approach to face verification based on the Error Correcting Output Coding (ECOC) classifier design concept. In the training phase the client set if 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 imposters. 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 sets.
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