A comparative study of face recognition algorithms under facial expression and illumination

Face recognition algorithms enable computational devices to automatically recognize human faces and have been adopted by many big tech companies. These algorithms have shown fairly acceptable performance in criminal identification, healthcare, advertisement, access and security, payments and other different areas. In this paper, we attempt comparatively to identify best algorithms in terms of accuracy when applied on datasets that have considerable facial expression and pose illumination challenges. We selected benchmark datasets and applied preprocessing techniques to suppress noise in the images. We then applied different algorithms for feature extraction and then feed these features to the classifier. Based on the conducted experiments, we observed that the Local Binary Pattern Histogram algorithm outperformed the other two selected algorithms by about 1% against Linear Discriminant Analysis (LDA) and about 9% against Principal Component Analysis (PCA). In addition, LDA outperformed the other two by obtaining about 99.532% of accuracy on JAFFE dataset.