Prototype selection for composite nearest neighbor classifiers
Prototype selection for composite nearest neighbor classifiers
Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Fusion of LDA and PCA for Face Verification
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Decoding rules for error correcting output code ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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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.