Recognising trajectories of facial identities using kernel discriminant analysis

Abstract

We present a comprehensive approach to address three challenging problems in face recognition: modelling faces across multi-views, extracting the non-linear discriminating features, and recognising moving faces dynamically in image sequences. A multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns. Kernel Discriminant Analysis, which employs the kernel technique to perform Linear Discriminant Analysis in a high-dimensional feature space, is developed to extract the significant non-linear features which maximise the between-class variance and minimise the within-class variance. Finally, an identity surface based face recognition is performed dynamically from video input by matching object and model trajectories.