3D AAM based face alignment under wide angular variations using 2D and 3D data

Active Appearance Models (AAMs) are widely used to estimate the shape of the face together with its orientation, but AAM approaches tend to fail when the face is under wide angular variations. Although it is feasible to capture the overall 3D face structure using 3D data from range cameras, the locations of facial features are often estimated imprecisely or incorrectly due to depth measurement uncertainty. Face alignment using 2D and 3D images suffer from different issues and have varying reliability in different situations. The existing approaches introduce a weighting function to balance 2D and 3D alignments in which the weighting function is tuned manually and the sensor characteristics are not taken into account. In this paper, we propose to balance 3D face alignment using 2D and 3D data based on the observed data and the sensors characteristics. The feasibility of wide-angle face alignment is demonstrated using two different sets of depth and conventional cameras. The experimental results show that a stable alignment is achieved with a maximum improvement of 26% compared to 3D AAM using 2D image and 30% improvement over the state-of-the-art 3DMM methods in terms of 3D head pose estimation.