Multiple classifier combination for face-based identity verification |
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Authors: | Jacek Czyz [Author Vitae] Josef Kittler [Author Vitae] [Author Vitae] |
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Affiliation: | a Communications Laboratory, Université catholique de Louvain, Place du Levant 2, Louvain-la-Neuve 1348, Belgium b Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, GU2 5XH, UK |
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Abstract: | When combining outputs from multiple classifiers, many combination rules are available. Although easy to implement, fixed combination rules are optimal only in restrictive conditions. We discuss and evaluate their performance when the optimality conditions are not fulfilled. Fixed combination rules are then compared with trainable combination rules on real data in the context of face-based identity verification. The face images are classified by combining the outputs of five different face verification experts. It is demonstrated that a reduction in the error rates of up to 50% over the best single expert is achieved on the XM2VTS database, using either fixed or trainable combination rules. |
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Keywords: | Face authentication Face verification Identity verification Multiple classifier systems Classifier combination A posteriori probability Linear discriminant analysis |
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