A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation

Fine-grain head pose estimation from imagery is an essential operation for many human-centered systems, including pose independent face recognition and human-computer interaction (HCI) systems. It is only recently that estimation systems have evolved past coarse level classification of pose and concentrated on fine-grain estimation. In particular, the state of the art of such systems consists of nonlinear manifold embedding techniques that capture the intrinsic relationship of a pose varying face dataset. The success of these solutions can be attributed to the acknowledgment that image variation corresponding to pose change is nonlinear in nature. Yet, the algorithms are limited by the complexity of embedding functions that describe the relationship. We present a pose estimation framework that seeks to describe the global nonlinear relationship in terms of localized linear functions. A two layer system (coarse/fine) is formulated on the assumptions that coarse pose estimation can be performed adequately using supervised linear methods, and fine pose estimation can be achieved using linear regressive functions if the scope of the pose manifold is limited. A pose estimation system is implemented utilizing simple linear subspace methods and oriented Gabor and phase congruency features. The framework is tested using widely accepted pose-varying face databases (FacePix(30) and Pointing'04) and shown to perform fine head pose estimation with competitive accuracy when compared with state of the art nonlinear manifold methods.