Iterative three-dimensional head pose estimation using a face normal vector

The performance of face recognition systems has much been burdened by head pose variation. To solve this problem, 3-D face recognition systems that make use of multiple views and depth information have been suggested. However, without an accurate head pose estimation, the performance improvement of 3-D face recognition systems under pose variations remains limited. Previous research on 3-D head pose estimation has been conducted in 3-D space, where the estimation complexity is high. Also it is difficult to incorporate those salient 2-D face features for effective estimation. We propose a novel iterative 3-D head pose estimation method incorporating both 2-D and 3-D face information. To verify the effectiveness, we apply the proposed method to 3-D face modeling and recognition systems with adaptation to various 3-D face data acquisition devices. Our experimental results show that the proposed method can be very effective in terms of modeling and recognition applications, particularly on combining different kinds of acquisition devices, which use different coordinates of origin and scale.