IEEE Trans Image Process. 2017 Apr;26(4):1708-1722. doi: 10.1109/TIP.2016.2639448. Epub 2016 Dec 29.
Accurate face registration is a key step for several image analysis applications. However, existing registration methods are prone to temporal drift errors or jitter among consecutive frames. In this paper, we propose an iterative rigid registration framework that estimates the misalignment with trained regressors. The input of the regressors is a robust motion representation that encodes the motion between a misaligned frame and the reference frame(s), and enables reliable performance under non-uniform illumination variations. Drift errors are reduced when the motion representation is computed from multiple reference frames. Furthermore, we use the L norm of the representation as a cue for performing coarse-to-fine registration efficiently. Importantly, the framework can identify registration failures and correct them. Experiments show that the proposed approach achieves significantly higher registration accuracy than the state-of-the-art techniques in challenging sequences.
准确的人脸配准是许多图像分析应用的关键步骤。然而，现有的配准方法容易受到时间漂移误差或连续帧之间的抖动的影响。在本文中，我们提出了一种迭代刚性配准框架，该框架使用训练好的回归器来估计配准的误差。回归器的输入是一种稳健的运动表示，它编码了失配帧与参考帧之间的运动，并能在非均匀光照变化下可靠地工作。当从多个参考帧计算运动表示时，可以减少漂移误差。此外，我们使用表示的 L 范数作为执行从粗到精配准的线索。重要的是，该框架能够识别配准失败并进行纠正。实验表明，与具有挑战性的序列中的最新技术相比，所提出的方法显著提高了配准精度。
