Two-Dimensional Whitening of Face Images for Improved PCA Performance
Abd‐Krim Seghouane, Navid Shokouhi · IEEE Signal Processing Letters · 2018
We address the problem of two-dimensional (2-D) whitening of face image matrices. The proposed method whitens the distribution of rows and columns of an image matrix. The main contribution of this letter is to investigate some aspects of recently published studies on this topic. We point out that existing methods on 2-D whitening for face recognition overlook the rows as potential random vectors, implying that the image matrix is only a collection of independent identically distributed column vectors. This study shows that this one-sided whitening approach relies on an incorrect assumption for image data. We show that one-sided whitening replaces the covariance matrix along one dimension with the identity matrix. Our proposed, truely 2-D, whitening transform does not enforce any such restriction on the row- or column-covariance matrices. A second contribution of our letter is to illustrate a method to confirm that the rows and columns of front-pose face images are in fact approximately normally distributed. This validation has not been addressed previously, although it plays a crucial role in deriving a linear whitening transform.