Face alignment is a crucial component in most face analysis systems. It focuses on identifying the location of several keypoints of the human faces in images or videos. Although several methods and models are available to developers in popular computer vision libraries, they still struggle with challenges such as insufficient illumination, extreme head poses, or occlusions, especially when they are constrained by the needs of real-time applications. Throughout this article, we propose a set of training strategies and implementations based on data augmentation, software optimization techniques that help in improving a large variety of models belonging to several real-time algorithms for face alignment. We propose an extended set of evaluation metrics that allow novel evaluations to mitigate the typical problems found in real-time tracking contexts. The experimental results show that the generated models using our proposed techniques are faster, smaller, more accurate, more robust in specific challenging conditions and smoother in tracking systems. In addition, the training strategy shows to be applicable across different types of devices and algorithms, making them versatile in both academic and industrial uses.

在大多数人脸分析系统中，人脸对齐是至关重要的组成部分。它着重于识别图像或视频中人脸几个关键点的位置。尽管流行的计算机视觉库中的开发人员可以使用几种方法和模型，但它们仍面临诸如照明不足，极端的头部姿势或遮挡等挑战，特别是在受到实时应用程序需求约束的情况下。在整个本文中，我们提出了一套基于数据增强，软件优化技术的训练策略和实施方案，这些技术和方法可帮助改善属于几种用于面部对齐的实时算法的多种模型。我们提出了一组扩展的评估指标，这些评估指标允许进行新颖的评估以缓解在实时跟踪环境中发现的典型问题。实验结果表明，使用我们提出的技术生成的模型在特定的挑战性条件下更快，更小，更准确，更健壮，并且在跟踪系统中更流畅。此外，该培训策略显示可适用于不同类型的设备和算法，从而使其在学术和工业用途中通用。