ANALYSIS OF PCA-BASED AND FISHER DISCRIMINANT-BASED IMAGE RECOGNITION ALGORITHMS

Abstract

One method of identifying images is to measure the similarity between images. This is accomplished by using measures such as the L 1 norm, L 2 norm, covariance, Mahalanobis distance, and correlation. These similarity measures can be calculated on the images in their original space or on the images projected into a new space. I discuss two alternative spaces in which these similarity measures may be calculated, the subspace created by the eigenvectors of the covariance matrix of the training data and the subspace created by the Fisher basis vectors of the data. Variations of these spaces will be discussed as well as the behavior of similarity measures within these spaces. Experiments are presented comparing recognition rates for different similarity measures and spaces using hand labeled imagery from two domains: human face recognition and classifying an image as a cat or a dog.