An multi-task head pose estimation algorithm

Abstract

Estimating head pose is a hot topic in facial behavior analysis and understanding. Most of the existing methods called two-stage method take head pose estimation and face detection as two separate tasks. In general, independent face boxes need to be proposed before head pose estimation. Such scheme is inefficient and has poor robustness. The existing estimation methods for head pose is lack of effective anti-noise design. In this paper, we propose a multi-task deep learning method, which integrate face detection and pose estimation together. Three kind of anti-interference strategy are proposed. Compared with the existing two-stage method, the proposed method can be performed with less consumption of resource. Benefited from the complementary characteristics of multi task joint learning, our proposed has higher accuracy. Experiments on several public datasets fully show that the attitude angle estimation error accuracy of our one stage algorithm reaches 1.96° (MAE). It is better than the existing state of the art method. The speed is twice as fast as that of the two-stage method.