Accurate 3D face modeling and recognition from RGB-D stream in the presence of large pose changes
We propose a 3D face modeling and recognition system using an RGB-D stream in the presence of large pose changes. In the previous work, all facial data points are registered with a reference to improve the accuracy of 3D face model from a low-resolution depth sequence. This registration often fails when applied to non-frontal faces. It causes inaccurate 3D face models and poor performance of matching. We address this problem by pre-aligning each input face (`frontalization') before the registration, which avoids registration failures. For each frame, our method estimates the 3D face pose, assesses the quality of data, segments the facial region, frontalizes it, and performs an accurate registration with the previous 3D model. The 3D-3D recognition system using accurate 3D models from our method outperforms other face recognition systems and shows 100% rank 1 recognition accuracy on a dataset with 30 subjects.