IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion in Multibiometric Identification Systems: What about the Missing Data?
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Combining pattern recognition modalities at the sensor level via kernel fusion
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
IEEE Transactions on Information Forensics and Security
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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It is commonly the case in multi-modal pattern recognition that certain modality-specific object features are missing in the training set. We address here the missing data problem for kernel-based Support Vector Machines, in which each modality is represented by the respective kernel matrix over the set of training objects, such that the omission of a modality for some object manifests itself as a blank in the modality-specific kernel matrix at the relevant position. We propose to fill the blank positions in the collection of training kernel matrices via a variant of the Neutral Point Substitution (NPS) method, where the term "neutral point" stands for the locus of points defined by the "neutral hyperplane" in the hypothetical linear space produced by the respective kernel. The current method crucially differs from the previously developed neutral point approach in that it is capable of treating missing data in the training set on the same basis as missing data in the test set. It is therefore of potentially much wider applicability. We evaluate the method on the Biosecure DS2 data set.