The locations of the fiducial facial landmark points around facial components and facial contour capture the rigid and non-rigid facial deformations due to head movements and facial expressions. They are hence important for various facial analysis tasks. Many facial landmark detection algorithms have been developed to automatically detect those key points over the years, and in this paper, we perform an extensive review of them. We classify the facial landmark detection algorithms into three major categories: holistic methods, Constrained Local Model (CLM) methods, and the regression-based methods. They differ in the ways to utilize the facial appearance and shape information. The holistic methods explicitly build models to represent the global facial appearance and shape information. The CLMs explicitly leverage the global shape model but build the local appearance models. The regression based methods implicitly capture facial shape and appearance information. For algorithms within each category, we discuss their underlying theories as well as their differences. We also compare their performances on both controlled and in the wild benchmark datasets, under varying facial expressions, head poses, and occlusion. Based on the evaluations, we point out their respective strengths and weaknesses. There is also a separate section to review the latest deep learning based algorithms. The survey also includes a listing of the benchmark databases and existing software. Finally, we identify future research directions, including combining methods in different categories to leverage their respective strengths to solve landmark detection “in-the-wild”.

中文翻译：

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人脸地标检测：文献调查

面部组件和面部轮廓周围的基准面部标志点的位置捕获由于头部运动和面部表情而导致的刚性和非刚性面部变形。因此，它们对于各种面部分析任务很重要。多年来，已经开发了许多面部标志检测算法来自动检测这些关键点，在本文中，我们对它们进行了广泛的回顾。我们将面部标志检测算法分为三大类：整体方法、约束局部模型 (CLM) 方法和基于回归的方法。它们在利用面部外观和形状信息的方式上有所不同。整体方法明确地构建模型来表示全局面部外观和形状信息。CLM 明确利用全局形状模型，但构建局部外观模型。基于回归的方法隐式捕获面部形状和外观信息。对于每个类别中的算法，我们讨论了它们的基本理论以及它们的差异。我们还比较了他们在不同面部表情、头部姿势和遮挡下在受控和野生基准数据集上的表现。根据评估，我们指出了他们各自的优势和劣势。还有一个单独的部分来回顾最新的基于深度学习的算法。该调查还包括基准数据库和现有软件的列表。最后，我们确定了未来的研究方向，