The effect of aging on face recognition using holistic and subpattern-based approaches is studied in this paper. The performance analysis of holistic and subpattern-based methods is presented on face recognition in the presence of age progression with specific preprocessing techniques. Original PCA, subspace LDA and subpattern-based counterparts of these holistic methods are used as feature extractors with the combination of the preprocessing techniques of histogram equalization and mean-and-variance normalization in order to nullify the effect of illumination changes which are known to significantly degrade recognition performance. The recognition performance of the holistic approaches is compared with the performance of subpattern-based PCA and subpattern-based subspace LDA approaches in order to demonstrate the performance differences and similarities between these two types of approaches. To be consistent with the research of others, our work has been tested on two publicly available databases namely FGNET and MORPH. The experiments are performed on these aging databases to demonstrate the recognition performances of the holistic and subpattern-based approaches in the presence of age differences on the faces.