The ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. First, a spatial transformer-generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment. Then, stacked hourglass network is employed to obtain preliminary locations of landmarks as well as their corresponding scores. In addition, an exemplar-based shape dictionary is designed to determine landmarks with low scores based on those with high scores. By incorporating face shape constraints, misaligned landmarks caused by occlusions or cluttered backgrounds can be considerably improved. Extensive experiments based on challenging benchmark datasets are performed to demonstrate the superior performance of the proposed method over other state-of-the-art methods.

概括不受约束的条件（例如严重的咬合和较大的姿势变化）的能力仍然是在面部对齐中要实现的具有挑战性的目标。本文提出了一种基于深度神经网络的多阶段模型，该模型利用了空间变换器网络，沙漏网络和基于示例的形状约束。首先，由卷积层和残差单元组成的空间互感器对抗网络被用于解决由面部检测器引起的初始化问题，例如旋转和比例变化，以获得用于面部对齐的改进的面部边界框。然后，使用堆叠的沙漏网络来获取地标的初步位置及其相应的分数。此外，基于示例的形状字典旨在根据得分较高的地标确定得分较低的地标。通过合并面部形状约束，可以显着改善由遮挡或背景混乱导致的路标不对齐。进行了基于具有挑战性的基准数据集的广泛实验，以证明所提出的方法优于其他最新方法的性能。