AN INFORMATION-THEORETIC APPROACH TO FACE RECOGNITION FROM FACE MOTION MANIFOLDS.
O. Arandjelović and R. Cipolla.
In this work we consider face recognition from Face Motion Manifolds (FMMs). The use of the Resistor-Average Distance (RAD) as a dissimilarity measure between densities confined to FMMs is motivated in the proposed information-theoretic approach to modelling face appearance. We introduce a kernel-based algorithm that makes use of the simplicity of the closed-form expression for RAD between two Gaussian densities, while allowing for modelling of complex and nonlinear, but intrinsically low-dimensional manifolds. Additionally, it is shown how geodesically local FMM structure can be modelled, naturally leading to a stochastic algorithm for generalizing to unseen modes of data variation. Recognition performance of our method is demonstrated experimentally and is shown to exceed that of state-of-the-art algorithms. Recognition rate of 98% was achieved on a database of 100 people under varying illumination.
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