Pose Estimation

Pose Estimation

Phase Space for Face Pose Estimation

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 developed 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 new 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 Discriminate Analysis (LDA), and conventional CCA.

Locally Tuned Nonlinear Manifold for Person Independent Head Pose Estimation

Fine pose estimation is performed by propagating through two layers of the model: layer 1 performs a neighborhood estimation using class-based supervised linear techniques, and layer 2 formulates a fine pose estimate using region dependent pose-regressive transforms.