深度学习人脸特征点自动定位综述  

Automatic facial feature points location based on deep learning:a review

作　　者：徐亚丽 赵俊莉 吕智涵 张志梅 李劲华 潘振宽 Xu Yali;Zhao Junli;Lyu Zhihan;Zhang Zhimei;Li Jinhua;Pan Zhenkuan(College of Computer Science&Technology,Qingdao University,Qingdao 266071,China)

机构地区：[1]青岛大学计算机科学技术学院,青岛266071

出　　处：《中国图象图形学报》2021年第11期2630-2644,共15页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62172247,61772294,61702293,61902203);全国统计科学研究项目(2020355);山东省重点研发计划重大科技创新工程项目(2019JZZY020101);山东省自然科学基金项目(ZR2019LZH002)。

摘　　要：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 per-formed 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 independ

关 键 词：深度学习 2维人脸特征点定位 3维人脸特征点定位 卷积神经网络(CNN) 配准 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]