Latent codebook regression for facial feature localisation

Abstract

A new non-parametric inferring procedure of facial feature localisation which learns regression in latent variable space is introduced. The proposed method uses regression between latent feature and motion spaces spanned by a set of bases of nonlinear feature space and motion vector space which is trained from a large sample set in codebook. Compared with the previous method of using codebook or eigen-codebook, the proposed method achieved both a significant reduction in the memory consumption without loss of accuracy and a very low computational complexity enough to run in mobile devices.