Post-processing of dimensionality reduction methods for face recognition

Pre-processing approaches have been widely used in face recognition to enhance images. However, a notably limited amount of research has examined the use of post-processing methods. In this paper, we propose a novel post-processing framework to improve dimensionality reduction methods for robust face recognition. The proposed method does not work on the features directly; it decomposes each feature into different components using multidimensional ensemble empirical mode decomposition and later maximizes the dependency and the dispersion among classes using a Gaussian function. The performance of the proposed algorithm is demonstrated through experiments by applying several dimensionality reduction techniques on two public databases.