Shape classification using smooth principal components

We suggest and assess a novel approach to shape classification using smooth functional principal components. This gives a rotation, location and scale invariant classifier. Our experiments show that this approach can outperform a number of competitors including conventional eigenshape methods and time series methods. The degree of smoothing associated with the best classification performance is determined automatically using cross-validation. (C) 2003 Elsevier Science B.V. All rights reserved.