Penalized image averaging and discrimination with facial and fishery applications

In this paper we use a penalized likelihood approach to image warping in the context of discrimination and averaging. The choice of average image is formulated statistically by minimizing a penalized likelihood, where the likelihood measures the similarity between images after warping and the penalty is a measure of distortion of a warping. The notions of measures of similarity are given in terms of normalized image information. The measures of distortion are landmark based. Thus we use a combination of landmark and normalized image information. The average defined in the paper is also extended by allowing random perturbation of the landmarks. This strategy improves averages for discrimination purposes. We give here real applications from medical and biological areas.