Autoassociative Neural Network based Active Shape Models

This paper presents an improved active shape model algorithm, that exploits auto associative neural networks (AANNs) to estimate the local feature models. The proposed technique aims at solving face feature localization tasks, nevertheless it can be used also in the more general case of object detection. Three main contributions are presented. The first one consists in the estimation of elliptic search areas by means of the training data. The second one is the use of AANNs as local feature detectors, since this network model is particularly suited to solve classification tasks with unbalanced classes. Finally, an optimized technique to set up the learning environment, needed to train the AANNs, is described. The performances of the proposed algorithm compare favorably with original ASMs and with two recent improved versions.