Combination of kernels applied to face verification

In this paper a novel method of information fusion at classifier level is applied to face verification. Three complementary kinds of facial data have been considered: texture, range data and curvature images. Three different kernels have been defined from each representation and finally a combined kernel has been developed. The resulting kernel has been used to train a classifier based on Support Vector Machines and it has been applied to face verification. The method has been deeply tested using the Face Recognition Grand Challenge database. The experiments show that in all cases the combined proposed classifier improves individual classifiers.