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State-of-the-art image-set matching techniques typically implicitly modeleach image-set with a Gaussian distribution. Here, we propose to go beyondthese representations and model image-sets as probability distributionfunctions (PDFs) using kernel density estimators. To compare and matchimage-sets, we exploit Csiszar f-divergences, which bear strong connections tothe geodesic distance defined on the space of PDFs, i.e., the statisticalmanifold. Furthermore, we introduce valid positive definite kernels on thestatistical manifolds, which let us make use of more powerful classificationschemes to match image-sets. Finally, we introduce a supervised dimensionalityreduction technique that learns a latent space where f-divergences reflect theclass labels of the data. Our experiments on diverse problems, such asvideo-based face recognition and dynamic texture classification, evidence thebenefits of our approach over the state-of-the-art image-set matching methods.

Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs