Supervised Selective Combining Pattern Recognition Modalities and Its Application to Signature Verification by Fusing On-Line and Off-Line Kernels
We consider the problem of multi-modal pattern recognition under the assumption that a kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels embed the output scales of single sensors is employed as an appropriate joint scale corresponding to the idea of combining modalities at the sensor level. This contrasts with the commonly adopted method of combining classifiers inferred from each specific modality. However, a significant risk in combining linear spaces is that of overfitting. To address this, we set out a stochastic method for encompassing modal-selectivity that is intrinsic to (that is to say, theoretically contiguous with) the selected kernel-based pattern-recognition approach.
The principle of kernel selectivity supervision is then applied to the problem of signature verification by fusing several on-line and off-line kernels into a complete training and verification technique.
KeywordsMultiple Kernel Learning Training Criterion Kernel Fusion Selective Fusion Skilled Forgery
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