An information-theoretic approach to face recognition from face motion manifolds

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. Key words: face recognition, face motion manifolds, kernel, resistor-average distanc...

• Type: Journal
• Year: 2006
• Where: IVC
• Authors: Ognjen Arandjelovic, Roberto Cipolla
• Keywords: Face Motion, IVC 2006, Manifolds, Resistor-average Distance