Employing Fuzzy-Histogram Equalization to Combat Illumination Invariance in Face Recognition Systems
With the recent surge in acceptance of face recognition systems, more and more work is needed to perfect the existing grey areas. A major concern is the issue of illumination intensities in the images used as probe and images trained in the database. This paper presents the adoption and use of fuzzy histogram equalization in combating illumination variations in face recognition systems. The face recognition algorithm used is Principal Component Analysis, PCA. Histogram equalization was enhanced using some fuzzy rules in order to get an efficient light normalization. The algorithms were implemented and tested exhaustively with and without the application of fuzzy histogram equalization to test the authors' approach. A good and considerable result was achieved.