Determining Discriminative Anatomical Point Pairings Using Adaboost For 3d Face Recognition

In this paper, we present a novel method for 3D face recognition using adaboosted geodesic distance features. Firstly, a generic model is finely conformed to each face model contained within a 3D face dataset. Secondly, the geodesic distance between anatomical point pairs are computed across each conformed generic model. Adaboost then generates a strong-classifier based on a collection of geodesic distances that are most discriminative for face recognition. Experiments conducted on the Face Recognition Grand Challenge (FRGC) database D collection indicate that the system can achieve over a 95% rank-one recognition rate.

• Type: Conference
• Year: 2009
• Where: ICIP
• Keywords: Face Model, Face Recognition, Geodesic Distance Features, ICIP 2009, Image Processing