Face recognition has recently become one of the most important areas of image processing. The current face recognition systems are capable of recognizing and identifying the subject of interest based on digitally acquired images or recordings.
Face recognition systems are being applied in numerous ways in security and early detection of suspicious individuals. The use of these systems is also increasing in large public places such as airports, stadiums, etc. Here, the key advantage of face recognition systems compared to other biometrics can be exploited, as such systems do not require the cooperation of the tested subject.
The main difficulties in the successful implementation of face recognition systems are changes in illumination conditions and subject pose variation.
Aldini eta al. in  proved that the variation between the images of the same face due to illumination and viewing direction are almost always larger than variations due to changes of the subject’s identity.
In this thesis, we analyzed and compared the photometric normalization techniques for face recognition applications. Five different preprocessing algorithms have been tested on the extended Yale Face Database B, namely, the single scale retinex algorithm, the single scale self-quotient image, the anisotropic diffusion based normalization technique, the single scale Weberfaces normalization technique and the Tan and Trigg’s normalization technique. Furthermore, we tested the influence of histogram equalization image enhancement technique on the above mentioned photometric normalization techniques. In the next step, we used the LDA (Linear Discriminant Analysis) algorithm to identify the best illumination invariant methods.
The biometrical system was assessed in the identification and verification operating mode. The results are presented in the form of rank 1 results of the identification and the receiver operating characteristic curve, as well as a percentage of false positive cases (FAR 1 % and 0,1 %) in the verification operating mode.
The algorithms are implemented in Matlab, with the use of the INface and PhD toolboxes which were developed at the Laboratory of Artificial Perception, Systems and Cybernetics at the Faculty of Electrical Engineering, University of Ljubljana.