二维Gabor特征与三维NP-3DHOG特征融合的人脸识别算法

摘要/Abstract

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。

关键词: 二维纹理特征, 人脸识别, 三维可辨别性特征, 特征融合, 梯度直方图特征

Abstract: 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.

Key words: 2D texture feature, 3D discriminant feature, Face recognition, Feature fusion, Histograms of oriented gradients

中图分类号:

TP242.6

王雪峤,齐华山,袁家政,梁爱华,孙力红. 二维Gabor特征与三维NP-3DHOG特征融合的人脸识别算法[J]. 计算机科学, 2018, 45(6A): 247-251. https://doi.org/

WANG Xue-qiao,QI Hua-shan, YUAN Jia-zheng, LIANG Ai-hua, SUN Li-hong. Face Recognition Using 2D Gabor Feature and 3D NP-3DHOG Feature[J]. Computer Science, 2018, 45(6A): 247-251. https://doi.org/

参考文献

[1]WANG Y,CHUA C S,HO Y K,et al.Integrated 2D and 3D Images for Face Recognition[C]∥11th International Conference on Image Analysis and Processing(ICIAP’01).2001:48-53.
[2]WANG Y,CHUA C S,HO Y K.Facial feature detection and face recognition from 2D and 3D images[J].Pattern Recognition Letters,2002,23(10):1191-1202.
[3]WANG Y,CHUA C S,HO Y K.Face recognition from 2D and 3D images using structural Hausdorff distance[C]∥7th International Conference on Control,Automation,Robotics and Vision (ICARCV).2002:502-507.
[4]TSALAKANIDOU F,TZOCARAS D,STRINTZIS M.Use of depth and color eigenfaces for face recognition[J].Pattern Re-cognition Letters,2003,24(9):1427-1435.
[5]CHANG K,BOWYER K,FLYNN P.Face recognition using 2D and 3D facial data[M]∥ACM Workshop on Multimodal User Authentication.2003:25-32.
[6]TSALAKANIDOU F,MALASSIOTIS S,STRINTZIS M G.Integration of 2D and 3D images for enhanced face authentication[C]∥Sixth International Conference on Automated Face and Gesture Recognition.2004:266-271.
[7]TSALAKANIDOU F,MALASSIOTIS S,STRINTZIS M G. Face localization and authentication using color and depth images[J].IEEE Transactions on Image Processing,2005,14(2):152-168.
[8]PAPATHEODOROU T,REUCKERT D.Evaluation of auto- matic 4D face recognition using surface and texture registration[C]∥Sixth International Conference on Automated Face and Gesture Recognition.2004:321-326.
[9]GODIL A,RESSLER S,GROTHER P.Face recognition using 3D facial shape and color map information:comparison and combination[J].SPIE Biometric Technology for Human Identification,2005,5404:351-361.
[10]BENABDELKADER C,GRIFFIN P A.Comparing and combining depth and texture cues for face recognition[J].Image and Vision Computing,2005,23(3):339-352.
[11]TSALAKANIDOU F,FORSTER F,MALASSIOTIS S,et al.Real-time acquisition of depth and color images using structured light and its application to 3D face recognition[J].Real-Time Imaging,2005,11(5／6):358-369.
[12]CHANG K I,BOWYER K W,FLYNN P J.An evaluation of multimodal 2D+3D face biometrics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(4):619-625.
[13]HUSKEN M,BRAUCKMANN M,GEHLEN S,et al.Strate- gies and benefits of fusion of 2D and 3D face recognition[C]∥Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05).2005.
[14]MALASSIOTIS S,STRINTZIS M G.Robust face recognition using 2D and 3D data:Pose and illumination compensation[J].Pattern Recognition,2005,38(12):2537-2548.
[15]LU X,JAIN A K.Integrating range and texture information for 3D face recognition[C]∥7th IEEE Workshop on Applications of Computer Vision (WACV’05).2005:155-163.
[16]WANG Y,CHUA C.Face recognition from 2D and 3D images using 3D Gabor filters[J].Image and Vision Computing,2005,23:1018-1028.
[17]WANG Y,CHUA C.Robust face recognition from 2D and 3D images using structural Hausdorff distance[J].Image and Vision Computing,2006,24(2):176-185.
[18]NANNI L,LUMINI A.Region Boost learning for 2D+3D based face recognition[J].Pattern Recognition Letters,2007,28(15):2063-2070.
[19]MIAN A,BENNAMOUN M,OWENS R.An efficient multimodal 2D-3D hybrid approach to automatic face recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(11):1927-1943.
[20]RICCIO D,DUGELAY J.Geometric invariants for 2D/3D face recognition[J].Pattern Recognition Letters,2007,28(14):1907-1914.
[21]WANG J,LIM E,CHEN X,et al.Real-time stereo face recognition by fusing appearance and depth fisherfaces[J].Journal of VLSI Signal Processing,2007,49(3):409-423.
[22]SUN T,CHEN M,LO S,et al.Face recognition using 2D and disparity eigenface[J].Expert Systems with Applications,2007,33(2):265-273.
[23]SUN T,TIEN F.Using back propagation neural network for face recognition with 2D+3D hybrid information[J].Expert Systems with Applications,2008,35(1／2):361-372.
[24]MAURER T,GUIGONIS D,MASLOV I,et al.Performance of geometrix activeID TM 3D face recognition engine on the FRGC data[C]∥2005 IEEE Computer Society Conference on Compu-ter Vision and Pattern Recognition.2005.
[25]MIAN A S,BENNAMOUN M,OWENS R A.Automatic 3D Face Detection,Normalization and Recognition[C]∥Third Int’l Symp.3D Data Processing,Visualization and Transmission.2006:735-742.
[26]BELHUMEUR P N,HESPANHA J P,KRIEGMAN D J. Eigenfaces versus fisherfaces:recognition using class specific li-near projection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[27]LIU C,WECHSLER H.Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J].IEEE Transactions on Image Processing,2002,11(4):467-476.
[28]DALAL N,TRIGGS B.Histograms of oriented gradients for human Detection[C]∥IEEE Conference on Computer Vision & Pattern Recognition.2005:886-893.

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