A Novel Probabilistic Linear Subspace Approach for Face Applications

摘要

Over the past several decades, pattern classification based on subspace methodology is one of the most attractive research topics in the field of computer vision. In this paper, a novel probabilistic linear subspace approach is proposed, which utilizes hybrid way to capture multidimensional data extracting maximum discriminative information and circumventing small eigenvalues by minimizing statistical dependence between components. During features extraction process, local region is emphasized for crucial patterns representation, and also statistic technique is used to regularize these unreliable information for both reducing computational cost and maintaining accuracy purposes. Our approach is validated with a high degree of accuracy with various face applications using challenging databases containing different variations.