Face Pose Estimation From Video Sequence Using Dynamic Bayesian Network

This paper describes a technique to estimate human face pose from color video sequence using dynamic Bayesian network(DBN). As face and facial features trackers usually track eyes, pupils, mouth corners and skin region(face), our proposed method utilizes merely three of these features - pupils, mouth center and skin region - to compute the evidence for DBN inference. No additional image processing algorithm is required, thus, it is simple and operates in real-time. The evidence, which are called horizontal ratio and vertical ratio in this paper, are determined using model-based technique and designed significantly to simultaneously solve two problems in tracking task; scaling factor and noise influence. Results reveal that the proposed method can be realized in real-time on a 2.2 GHz Celeron CPU machine with very satisfactory pose estimation results.