An end-to-end network architecture, the Recurrent Shape Regression (RSR), is presented to deal with the task of facial shape detection, a crucial step in many computer vision problems. The RSR generalizes the conventional cascaded regression into a recurrent dynamic network through abstracting common latent models with stage-to-stage operations. Instead of invariant regression transformation, we construct shape-dependent dynamic regressors to attain the recurrence of regression action itself. The regressors can be stacked into a high-order regression network to represent more complex shape regression. By further integrating feature learning as well as global shape constraint, the RSR becomes more controllable in entire optimization of shape regression, where the gradient computation can be efficiently back-propagated through time. To handle the possible partial occlusions of shapes, we propose a mimic virtual occlusion strategy by randomly disturbing certain point cliques without the requirement of any annotations of occlusion information or even occluded training data. Extensive experiments on five face datasets demonstrate that the proposed RSR outperforms the recent state-of-the-art cascaded approaches.

中文翻译：

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循环形状回归。

提出了一种端到端的网络体系结构，即递归形状回归（RSR），以处理面部形状检测的任务，这是许多计算机视觉问题中的关键步骤。RSR通过使用阶段到阶段的操作抽象通用的潜在模型，将常规的级联回归一般化为递归动态网络。代替不变的回归变换，我们构造依赖于形状的动态回归器，以实现回归动作本身的重现。可以将这些回归变量堆叠到一个高阶回归网络中，以表示更复杂的形状回归。通过进一步集成特征学习以及整体形状约束，RSR在形状回归的整个优化中变得更加可控，其中梯度计算可以随着时间有效地向后传播。为了处理形状的可能部分遮挡，我们提出了一种模拟虚拟遮挡策略，该方法通过随机扰动某些点组而无需遮挡信息的任何注释，甚至不需要遮挡的训练数据。在五个人脸数据集上进行的广泛实验表明，所提出的RSR优于最新的最新级联方法。