Head pose estimation by non-linear embedding and mapping
In this paper, we present a new scheme to robustly estimate the head pose from either video sequence or individual images. Developed from ISOMAP, we learn a person-independent and nonlinear embedding space (we call it a 2-D feature space) for different poses. A nonlinear interpolation is proposed to map new sequences or images into the 2-D feature space. Especially for video sequences, we propose an adaptive local fitting technique to filter unreasonable mappings. By exploring the intrinsic characteristics, we further estimate the head pose of that sequence or image. Experiments reported in this paper showed robust results.