Facial expression recognition has seen rapid development in recent years due to its wide range of applications such as human–computer interaction, health care, and social robots. Although significant progress has been made in this field, it is still challenging to recognize facial expressions with occlusions and large head-poses. To address these issues, this paper presents a cascade regression-based face frontalization (CRFF) method, which aims to immediately reconstruct a clean, frontal and expression-aware face given an in-the-wild facial image. In the first stage, a frontal facial shape is predicted by developing a cascade regression model to learn the pairwise spatial relation between non-frontal face-shape and its frontal counterpart. Unlike most existing shape prediction methods that used single-step regression, the cascade model is a multi-step regressor that gradually aligns non-frontal shape to its frontal view. We employ several different regressors and make a ensemble decision to boost prediction performance. For facial texture reconstruction, active appearance model instantiation is employed to warp the input face to the predicted frontal shape and generate a clean face. To remove occlusions, we train this generative model on manually selected clean-face sets, which ensures generating a clean face as output regardless of whether the input face involves occlusions or not. Unlike the existing face reconstruction methods that are computational expensive, the proposed method works in real time, so it is suitable for dynamic analysis of facial expression. The experimental validation shows that the ensembling cascade model has improved frontal shape prediction accuracy for an average of 5% and the proposed method has achieved superior performance on both static and dynamic recognition of facial expressions over the state-of-the-art approaches. The experimental results demonstrate that the proposed method has achieved expression-preserving frontalization, de-occlusion and has improved performance of facial expression recognition.

面部表情识别由于其广泛的应用（例如人机交互，医疗保健和社交机器人）而在近几年得到了快速发展。尽管在该领域已经取得了重大进展，但是识别具有遮挡物和大头姿势的面部表情仍然具有挑战性。为了解决这些问题，本文提出了一种基于级联回归的人脸正面化（CRFF）方法，该方法旨在在给定野外面部图像的情况下立即重建干净，正面和表情敏感的人脸。在第一阶段，通过建立级联回归模型来学习非额脸形状与其正面对应物之间的成对空间关系，从而预测额脸形状。与大多数现有的使用单步回归的形状预测方法不同，级联模型是一个多步回归器，可逐步将非前额形状与其正视图对齐。我们使用几种不同的回归变量，并做出整体决策以提高预测性能。对于面部纹理重建，采用主动外观模型实例化将输入面部变形为预测的正面形状并生成干净的面部。为了消除遮挡，我们在手动选择的清洁面部集合上训练了该生成模型，从而确保生成清洁面部作为输出，而不管输入面部是否涉及遮挡。与现有的计算量大的面部重建方法不同，该方法可实时工作，因此适用于面部表情的动态分析。实验验证表明，该级联模型提高了平均5％的额叶形状预测精度，并且与最新方法相比，该方法在静态和动态识别面部表情方面均具有出色的性能。实验结果表明，该方法实现了保持表情的正面化，去遮挡并提高了面部表情识别的性能。