人脸特征点定位是根据输入的人脸数据自动定位出预先按人脸生理特征定义的眼角、鼻尖、嘴角和脸部轮廓等面部关键特征点，在人脸识别和分析等系统中起着至关重要的作用。本文对基于深度学习的人脸特征点自动定位进行综述，阐释了人脸特征点自动定位的含义，归纳了目前常用的人脸公开数据集，系统阐述了针对2维和3维数据特征点的自动定位方法，总结了各方法的研究现状及其应用，分析了当前人脸特征点自动定位技术在深度学习应用中的现状、存在问题及发展趋势。在公开的2维和3维人脸数据集上对不同方法进行了比较。通过研究可以看出，基于深度学习的2维人脸特征点的自动定位方法研究相对比较深入，而3维人脸特征点定位方法的研究在模型表示、处理方法和样本数量上都存在挑战。未来基于深度学习的3维人脸特征点定位方法将成为研究趋势。

Face feature point location is to locate the predefined key facial feature points automatically according to the physiological characteristics of the human face, such as eyes, nose tip, mouth corner, and face contour. It is one of the important problems in face registration, face recognition, 3D face reconstruction, craniofacial analysis, craniofacial registration, and many other related fields. In recent years, various algorithms for facial feature point localization have emerged constantly, but several problems remain in the calibration of feature points, especially in the calibration of 3D facial feature points, such as manual intervention, low or inaccurate number of feature points, and long calibration time. In recent years, convolutional neural networks have been widely used in face feature point detection. This study focuses on the analysis of automatic feature point location methods based on deep learning for 2D and 3D facial data. Training data with real feature point labels in 2D texture image data are abundant. The research of automatic location method of 2D facial feature points based on deep learning is relatively extensive and indepth. The classical methods for 2D data include cascade convolution neural network methods, end-to-end regression methods, auto encoder network methods, different pose estimation methods, and other improved convolutional neural network (CNN) methods. In cascaded regression methods, rough detection is performed first, and then the feature points are finetuned. The end-to-end method propagates the error between the real results and the predicted results until the model converges. Autoencoder methods can select features automatically through encoding and decoding. Head pose estimation has great importance for face feature point detection because image-based methods are always affected by illumination and pose.Head pose estimation and feature points detection is improved by modifying network structure and loss function. The disadvantage of cascade regression method is that it can update the regressor by independent learning, and the descent direction may cancel each other. The flexibility of the end-to-end model is low. CNN is applied to 2D training data with real feature point tags. However, in the case of a 3D,training data with rich real feature point labels are lacking. Therefore, compared with 2D facial feature points, 3D facial feature point location remains a challenge. Several automatic feature point location for 3D data are introduced. The methods for 3D data are mainly based on depth information and 3D morphable model (3DMM). In recent years, with the development of RGB+depth map (RGBD) technology, depth data have attracted more attention. Feature point detection based on depth information has become an important preprocessing step for automatic feature point detection in 3D data. Initialization is crucial for deep data, but information is easily lost. The method based on 3DMM represents 3D face data for locating feature points through deep learning. On the one hand, the shape and expression parameters of 3DMM are highly nonlinear with the image texture information, which makes image mapping difficult to estimate. Compared with 2D face data, 3D face data lack training data with remarkable changes in face shape, race, and expression. Face feature point detection still faces great challenges.In summary, this study explains the meaning of automatic location of facial feature points, summarizes the currently open and commonly used face datasets, introduces various methods of automatic location of feature points for 2D and 3D data, summarizes the research status and application of each domestic and international method, analyzes the problems and development trend of automatic location technology of face feature points in deep learning application on 2D and 3D datasets, and compares the experimental results of the latest methods. In conclusion, the research on automatic location method of 2D face feature points based on deep learning is relatively indepth. Challenges in processing 3D data remain. The current solution for locating feature points is to project 3D face data onto 2D images through cylindrical coordinates, depth maps, 3DMM, and other methods. Information loss is the main problem of these methods. The method of feature point location directly on 3D model needs further exploration and research. The accuracy and speed of feature point location also need to be improved. In the future, 3D facial feature point localization methods based on deep learning will gradually become a trend.