A Comparative Study of PCA and LBP for Face Recognition Under Illumination Variations

Changes in lighting conditions are an important factor for facial recognition applications. The algorithms used in these applications have various approaches and are directly affected by environments under difficult lighting settings. In this study, we investigate two appearance-based local and global approaches, namely Principal Component Analysis and Local Binary Patterns algorithms, examine important studies on these algorithms and compare their facial recognition performances on images from the Extended Yale Face Database B. Experiments show that the LBP method provides better results under varying lighting conditions.