Face recognition using curvilinear component analysis

Automated face recognition can be applied in a number of situations including personal identification, mug shot matching, store security, and crowd surveillance. A large number of techniques based on linear methods of dimensionality reduction, such as principal component analysis (PCA), have recently been proposed. Motivated by the possibility of increased performance, we pursue in this paper a face recognition paradigm based on nonlinear methods of dimensionality reduction. More specifically, we use the recently proposed curvilinear component analysis (CCA) to obtain a reduced dimension representation of face images. Two types of classifiers, a k-NN classifier and a pseudo-inverse rule based classifier are used for assigning class labels to sample vectors in the reduced dimension space. The algorithm is found to be much faster and has better performance than a linear PCA based approach.