A comparison of PCA and ICA for object recognition under varying illumination

An experiment is performed to evaluate the ability of two different subspace methods to recognize objects under different illumination conditions. The principal component analysis (PCA) and independent component analysis (ICA) are compared for classifying 25 different objects with varying degrees of specularity under different illumination. Each object was sampled under three widely different lighting conditions to form a set of training images used to create subspaces with dimensions ranging from 10 to 30 basis vectors. The efficacy of ICA and PCA to correctly classify the objects was tested using two test images for each object under unique lighting conditions not included in the training set. The results were also determined when the images were pre-filtered with a Laplacian of Gaussian filter. Results show that ICA techniques show promise for object recognition under varying illumination conditions.