液晶与显示2018，Vol.33Issue(4)：306-316,11.DOI:10.3788/YJYXS20183304.0306
一种多特征相结合的三维人脸关键点检测方法
A key point detection method of 3D face based on multi-feature
摘要
Abstract
Key point detection plays an important role in the process of 3D face recognition.In order to improve the accuracy of key points detection,a new method of 3D face key points detection based on multi feature is proposed.Firstly,the key points of the 3d face of the training set are manually marked,and the different eigenvalues of each point of 3D face are calculated,and the mean and vari-ance of each feature are obtained for each key point.Secondly,the linear discriminant analysis is car-ried out on the characteristics of key points and non-critical points,thus get the score-weighted vector associated with each key point.The mean,variance,and score-weighted vectors of the previous ones are the output of the offline training.Finally,for an input model,we can get the candidate points of each key point combined the results of offline training,and construct the face structure model using these candidate points.According to the absolute distance constraint,relative position constraint, FLM model consistency classification,spin map and other methods we can determine the final point. In the experimental part,we select the three data sets of different postures,different expressions, gestures and expressions mixed from CASIA-3D FaceV1 and FRGCV2.0 database to detect the key points.Experiment results show that the detection rate of different gestures was 94.5%,and the de-tection rate of different expressions was 94%.Compared with other literatures,the detection rate in-creases by 20% on average.In addition,the algorithm has higher computing efficiency.关键词
有效能量/鼻尖点检测/姿态校正/测地距离/迭代最近点/主成分分析/分类器Key words
effective energy/nasal tip detection/posture correction/geodesic distance/iterative closest point/principal component analysis/classifier分类
信息技术与安全科学引用本文复制引用
冯超,陈清江..一种多特征相结合的三维人脸关键点检测方法[J].液晶与显示,2018,33(4):306-316,11.基金项目
国家自然科学基金(No.61403298) （No.61403298）
陕西省自然科学基金(No.2015JM1024)Supported by National Natural Science Fund(No.61403298) （No.2015JM1024）
Shaanxi Natural Science Foundation(No.2015JM1024) （No.2015JM1024）
