Efficient 3D face reconstruction from low quality video

3D shape information is crucial in many video analytic applications, such as face recognition and expression analysis. However, most commercial 3D modeling systems rely on dedicated equipment, which lacks operational flexibility. We present an efficient approach to reconstruct 3D face from low quality video, concentrated on recovering the depth information lost in imaging process. There are two novelties in the proposed method. First, depth error is explicitly estimated, which ensures a fully linear shape recovery process. Second, the shape is adjusted locally using local feature analysis (LFA) model, which effectively alleviates the model dominance problem. A prototype system is established based on the proposed approach, and evaluated on a publicly available database. Experimental results show that, compared with state-of-the-art approaches, our method increase accuracy of estimated shape in an efficient way.