二维Gabor特征与三维NP-3DHOG特征融合的人脸识别算法  

Face Recognition Using 2D Gabor Feature and 3D NP-3DHOG Feature

作　　者：王雪峤 齐华山[1] 袁家政[1] 梁爱华[1] 孙力红[1] WANG Xue-qiao, QI Hua- shan, YUAN Jia-zheng, HANG Ai- hua ,SUN Li- hong(Beijing Union University,Beijing 100101 ,Chin)

机构地区：[1]北京联合大学,北京100101

出　　处：《计算机科学》2018年第B06期247-251,共5页Computer Science

基　　金：北京市教育委员会科技计划一般项目(KM201811417002);北京联合大学基金(Zk10201603)资助

摘　　要：基于二维图像的人脸识别算法提取人脸纹理特征进行识别,但是光照、表情、人脸姿态等会对其产生不利影响。三维人脸特征能更精确地描述人脸的几何结构,并且不易受化妆和光照的影响,但只采用三维人脸数据进行人脸识别又缺少人脸纹理信息,因此文中将二维人脸特征与三维人脸特征相融合进行人脸识别。采用基于Gabor变换的二维特征与基于新的分块策略的三维梯度直方图特征相融合的算法进行人脸识别。首先,提取二维人脸的Gabor特征;然后,提取三维人脸基于新的分块策略的三维梯度直方图特征,旨在提取人脸的可辨别性特征;接下来,对二维人脸特征与三维人脸特征分别使用线性判别分析子空间算法进行训练,并使用加法原则融合两种特征的相似度矩阵;最后,输出识别结果。Face recognition algorithm based on 2D images extracts texture feature for recognition,but lighting,facial expressions and facial gestures can have adverse effect on it.3D face features can accurately describe the geometric structure of face and they are barely affected by makeup and light.Because 3D face feature lacks texture information,two kinds of features for face recognition.This paper fused Gabor based 2D face feature and new partitioning 3D histograms of oriented gradients 3D feature for face recognition.Firstly,the Gabor feature of 2D face is extracted,then the new partitioning 3D histograms of oriented gradients feature are extracted,which aims to extract the discriminant 3D face feature.Secondly,the linear discriminant analysis subspace algorithm is used to train two subspaces respectively.Finally,sum rule is used to fuse the two similarity matrices,and the nearest neighbor classifier is applied to finish the recognition process.

关 键 词：梯度直方图特征 二维纹理特征 三维可辨别性特征 人脸识别 特征融合 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]