Sub-intrapersonal space analysis for face recognition

Bayesian subspace analysis has been successfully applied in face recognition. However, it suffers from its operating on a whole face difference and using one global linear subspace to represent the similarity model. We develop a novel approach to address these problems. The proposed method operates directly on a set of partitioned local regions of the global face differences, and a separate Gaussian distribution is used to model each sub-intrapersonal space, accordingly. By combining all the local models, we can represent the complex intrapersonal variations more accurately. We further improve the system performance by reducing the contribution of local subspaces containing large variations using a smoothing method. The experiments on several standard face sets show that the proposed method is competitive.