Face Recognition Using Pca, Lda and Ica Approaches on Colored Images

In this paper, the performances of appearance-based statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are tested and compared for the recognition of colored face images. Three sets of experiments are conducted for relative performance evaluations. In the first set of experiments, the recognition performances of PCA, LDA and ICA are demonstrated. The effect of illumination variations is evaluated in the second set, whereas input images are partially occluded in the third set of experiments. The results show that PCA is better than LDA and ICA under different illumination variations but LDA is better than ICA. On the other hand, LDA is more sensitive than PCA and ICA on partial occlusions, but PCA is less sensitive to partial occlusions compared to LDA and ICA sensitivity. That is, PCA performance is better than LDA and ICA while performance of ICA performance is better than LDA on partial occlusions.