Illumination invariant feature extraction based on natural images statistics: Taking face images as an example

Natural images are known to carry several distinct properties which are not shared with randomly generated images. In this article we utilize the scale invariant property of natural images to construct a filter which extracts features invariant to illumination conditions. In contrast to most of the existing methods which assume that such features lie in high frequency part of spectrum, by analyzing the power spectra of natural images we show that some of these features could lie in low frequency part as well. From this fact, we derive a Wiener filter approach to best separate the illumination-invariant features from an image. We also provide a linear time algorithm for our proposed Wiener filter, which only involves solving linear equations with narrowly banded matrix. Our experiments on variable lighting face recognition show that our proposed method does achieve the best recognition rate and is generally faster compared to the state-of-the-art methods.