Face Verification Using Error Correcting Output Codes
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příspěvek z konference - tištěnýAuthor
Kittler, J.
Ghaderi, R.
Windeatt, T.
Matas, J.
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© 2001 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Metadata
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The error correcting output coding (ECOC) approach to classifier design decomposes a multi-class problem into a set of complementary two-class problems. We show how to apply the ECOC concept to automatic face verification, which is inherently a two-class problem. The output of the binary classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns representing clients and impostors. We propose two different combining strategies as the matching score for face verification. The first uses the first order Minkowski metric, and requires a threshold to be set. The second is a kernel-based method and has no parameters to set. The proposed method exhibits better performance on the well known XM2VTS data set compared with previous reported results.
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