Phase space for face pose estimation

Abstract

Face pose estimation from standard imagery remains a complex computer vision problem that requires identifying the primary modes of variance directly corresponding to pose variation, while ignoring variance due to face identity and other noise factors. Conventional methods either fail to extract the salient pose defining features, or require complex embedding operations. We propose a new method for pose estimation that exploits oriented Phase Congruency (PC) features and Canonical Correlation Analysis (CCA) to define a latent pose-sensitive subspace. The oriented PC features serve to mitigate illumination and identity features present in the imagery, while highlighting alignment and pose features necessary for estimation. The proposed system is tested using the Pointing'04 face database and is shown to provide better estimation accuracy than similar methods including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and conventional CCA. © 2010 Springer-Verlag.