Continuous miniaturization of mobile devices has greatly increased its adoption and use by people in various facets of our lives. This has also increased the popularity of face recognition and image processing. Face recognition is now being employed for security purpose opening up the need for further research in recent time. Image compression becomes useful in cases when images need to be transmitted across networks in a less costly way by increasing data volume while reducing transmission time. This work discusses our findings on image compression and its effect on face recognition systems. We studied and implemented three well known face recognition algorithms and observed their recognition accuracy when gallery / probe images were compressed and/or uncompressed as one would naturally expect. For compression purposes, we adopted the JPEG and JPEG2000 coding standard. The face recognition algorithms studied are PCA, ICA and LDA. As a form of an extensive research, experiments conducted include both in compressed and uncompressed domains where the three algorithms have been exhaustively analyzed. We statistically present the results obtained which showed no significant depreciation in the recognition accuracies.

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 our approach. A good and considerable result was achieved.