This paper introduces a novel real-time algorithm for facial landmark
tracking. Compared to detection, tracking has both additional challenges and
opportunities. Arguably the most important aspect in this domain is updating a
tracker's models as tracking progresses, also known as incremental (face)
tracking. While this should result in more accurate localisation, how to do
this online and in real time without causing a tracker to drift is still an
important open research question. We address this question in the cascaded
regression framework, the state-of-the-art approach for facial landmark
localisation. Because incremental learning for cascaded regression is costly,
we propose a much more efficient yet equally accurate alternative using
continuous regression. More specifically, we first propose cascaded continuous
regression (CCR) and show its accuracy is equivalent to the Supervised Descent
Method. We then derive the incremental learning updates for CCR (iCCR) and show
that it is an order of magnitude faster than standard incremental learning for
cascaded regression, bringing the time required for the update from seconds
down to a fraction of a second, thus enabling real-time tracking. Finally, we
evaluate iCCR and show the importance of incremental learning in achieving
state-of-the-art performance. Code for our iCCR is available from
this http URL

本文介绍了一种新颖的实时算法用于人脸关键点跟踪。我们提出了级联连续回归（CCR）算法，证明其准确性等同于 Supervised Descent Method，并提出了用于 CCR 的增量学习更新（iCCR），其速度比级联回归的标准增量学习快一个数量级，从而实现实时跟踪，最后评估了 iCCR 的重要性在实现最新技术水平方面。

实时增量人脸跟踪的级联连续回归

Cascaded Continuous Regression for Real-time Incremental Face Tracking

Many computer vision problems (e.g., camera calibration, image alignment,
structure from motion) are solved with nonlinear optimization methods. It is
generally accepted that second order descent methods are the most robust, fast,
and reliable approaches for nonlinear optimization of a general smooth
function. However, in the context of computer vision, second order descent
methods have two main drawbacks: (1) the function might not be analytically
differentiable and numerical approximations are impractical, and (2) the
Hessian may be large and not positive definite. To address these issues, this
paper proposes generic descent maps, which are average "descent directions" and
rescaling factors learned in a supervised fashion. Using generic descent maps,
we derive a practical algorithm - Supervised Descent Method (SDM) - for
minimizing Nonlinear Least Squares (NLS) problems. During training, SDM learns
a sequence of decent maps that minimize the NLS. In testing, SDM minimizes the
NLS objective using the learned descent maps without computing the Jacobian or
the Hessian. We prove the conditions under which the SDM is guaranteed to
converge. We illustrate the effectiveness and accuracy of SDM in three computer
vision problems: rigid image alignment, non-rigid image alignment, and 3D pose
estimation. In particular, we show how SDM achieves state-of-the-art
performance in the problem of facial feature detection. The code has been made
available at www.humansensing.cs.cmu.edu/intraface.

该论文提出了一种通用下降图的算法 ——SDM，通过学习下降方向和缩放因子来最小化 NLS 问题，在面部特征检测等计算机视觉问题中达到了最先进的性能。