基于局部多特征融合的三维人脸识别方法  

3D face recognition method based on local multi-feature fusion

作　　者：王金伟 胡步发[2] WANG Jinwei;HU Bufa(College of Intelligent Manufacturing Engineering,Liming Vocational University,Quanzhou 362000,China;College of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]黎明职业大学智能制造工程学院,福建泉州362000 [2]福州大学机械工程及自动化学院,福州350108

出　　处：《延边大学学报（自然科学版）》2022年第3期242-245,260,共5页Journal of Yanbian University（Natural Science Edition）

基　　金：黎明职业大学校课题(LZ2019113);福建省中青年教师教育科研项目(JZ180904)。

摘　　要：为了克服表情变化对三维人脸识别的影响,提出一种基于局部多特征融合的三维人脸识别方法.该方法首先根据中心侧影线提取鼻尖点,并以鼻尖点作为基准点制定窗口;然后利用形状索引值在窗口内提取关键点,并计算每个关键点和其区域的多维度特征后将其融合成特征向量;最后采用相似度匹配方法进行人脸识别,并以匹配点数最多的特征向量作为最终的识别结果.实验结果表明,该方法的识别率到达97.7%,且具有较好鲁棒性,同时优于文献[4]、[6]和文献[7]的方法;因此,该方法可为有效解决表情变化对三维人脸识别的影响提供参考.In order to overcome the influence of expression changes on 3D face recognition,a 3D face recognition method based on local multi-feature fusion is proposed.Firstly,the method extracts the nose tip according to the center profile line,and uses the nose tip as the reference point to formulate a window;then uses the shape index value to extract key points in the window,and calculates the multi-dimensional features of each key point and its area to fuse them into a Feature vector.Finally,the similarity matching method is used for face recognition,and the feature vector with the most matching points is used as the final recognition result.The experimental results show that the recognition rate of this method reaches 97.7%,and it has good robustness,which is better than the methods in the literature[4],[6]and[7].Therefore,this method can provide a reference for effectively solving the influence of expression changes on 3D face recognition.

关 键 词：三维人脸识别 窗口 关键点 特征向量 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]