General illumination correction and its application to face normalization

The appearance of an object can be severely affected by illumination. Thus, illumination correction is necessary both for human perception and machine recognition. The paper reports on a general approach for fast illumination correction. The approach has been tested for application in face normalization as a preprocessing step in face recognition. The basic idea of the algorithm is to normalize the image contrast locally using an affine transformation lighting model based on local estimation of background and gain. The background is estimated via an efficient multi-resolution low-pass filter and the gain is estimated via homomorphic filtering. This is followed by normalizing the data with the help of a clipped histogram. Experiments on images with different lighting conditions produce results that are better than those from using several popular illumination correction methods.