[1]杨恢先,付宇,曾金芳,等.基于正交Log-Gabor滤波二值模式的人脸识别算法[J].智能系统学报,2019,14(2):330-337.[doi:10.11992/tis.201708015]
YANG Huixian,FU Yu,ZENG Jinfang,et al.Face recognition based on orthogonal Log-Gabor binary pattern[J].CAAI Transactions on Intelligent Systems,2019,14(2):330-337.[doi:10.11992/tis.201708015]
点击复制
YANG Huixian,FU Yu,ZENG Jinfang,et al.Face recognition based on orthogonal Log-Gabor binary pattern[J].CAAI Transactions on Intelligent Systems,2019,14(2):330-337.[doi:10.11992/tis.201708015]
基于正交Log-Gabor滤波二值模式的人脸识别算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第2期
页码:
330-337
栏目:
学术论文—机器感知与模式识别
出版日期:
2019-03-05
- Title:
- Face recognition based on orthogonal Log-Gabor binary pattern
- Keywords:
- face recognition; Log-Gabor filter; collaborative representation; orthogonality; sparse coding; binary pattern; single sample; multi scale
- 分类号:
- TP391.4
- 摘要:
- 为消除可变光照对人脸识别的影响，提出一种基于正交Log-Gabor滤波二值模式（OLGBP）的人脸识别算法。该算法对样本在正交方向做Log-Gabor变换，然后将所得特征图像进行虚实分解和同尺度多方向二值融合构成OLGBP特征向量，再将这些特征向量构成协同表征字典D。最后，在字典D下对测试样本采用协同表征求稀疏系数，并通过误差重构来分类。在AR、Extend Yale B和CAS-PEAL-R1人脸数据库上的实验结果表明，OLGBP算法对光照变化的单样本人脸识别具有较好的效果，从而验证了算法的有效性。
- Abstract:
- To eliminate the effect of varying illumination on face recognition, a novel method of face recognition based on orthogonal log-Gabor binary pattern (OLGBP) is proposed in this paper. First, the algorithm performs log-Gabor transform on the samples in the orthogonal direction. Then the log-Gabor feature image is decomposed into real and imaginary parts, and the OLGBP feature vectors are constructed by fusing them into a binary pattern in the same scale at different directions. These feature vectors then form a collaboratively representative dictionary D. Finally, sparse coefficients are obtained by collaboratively representing these feature vectors with the test samples based on the dictionary D, and the test samples are classified by reconstruction of errors. The results for experiments performed on AR, Extend Yale B, and CAS-PEAL-R1 face databases show that the OLGBP algorithm has good effect on a single sample with illumination variation, and the effectiveness of the algorithm is verified.
- 参考文献/References:
-
[1] ADINI Y, MOSES Y, ULLMAN S. Face recognition:the problem of compensating for changes in illumination direction[J]. IEEE transactions on pattern analysis and machine intelligence, 1997, 19(7):721-732.
[2] TURK M, PENTLAND A. Eigenface for recognition[J]. Journal of cognitive neuroscience, 1991, 3(1):71-86.
[3] BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. fisherfaces:recognition using class specific linear projection[J]. IEEE transactions on pattern analysis and machine intelligence, 1997, 19(7):711-720.
[4] HE Xiaofei, YAN Shuicheng, HU Yuxiao, et al. Face recognition using laplacianfaces[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 27(3):328-340.
[5] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE transactions on pattern analysis and machine intelligence, 2009, 31(2):210-227.
[6] ZHANG Lei, YANG Meng, FENG Xiangchu. Sparse representation or collaborative representation:Which helps face recognition?[C]//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona, Spain, 2011:471-478.
[7] ZHAO Jia, WANG Yanjiang, LIU Baodi. Kernel collaborative representation for face recognition[C]//Proceedings of the 12th International Conference on Signal Processing. Hangzhou, China, 2014:1423-1427.
[8] LIU Weiyang, LU Lijia, LI Hui, et al. A novel kernel collaborative representation approach for image classification[C]//Proceedings of 2014 IEEE International Conference on Image Processing. Paris, France, 2014:4241-4245.
[9] YANG Meng, ZHANG Lei, FENG Xiangchu, et al. Fisher discrimination dictionary learning for sparse representation[C]//Proceedings of 2011 International Conference on Computer Vision. Barcelona, Spain, 2011:543-550.
[10] CHEN Yefei, SU Jianbo. Sparse embedded dictionary learning on face recognition[J]. Pattern recognition, 2017, 64:51-59.
[11] SUN Yi, WANG Xiaogang, TANG Xiaoou. Deep learning face representation from predicting 10,000 classes[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:1891-1898.
[12] YI Dong, LEI Zhen, LIAO Shengcai, et al. Learning face representation from scratch[J]. Computer science, arXiv:1411.7923, 2014.
[13] CANDèS E J, LI Xiaodong, MA Yi, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58(3):11.
[14] ZHANG Wenchao, SHAN Shiguang, GAO Wen, et al. Local Gabor binary pattern histogram sequence (LGBPHS):a novel non-statistical model for face representation and recognition[C]//Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005, 1:786-791.
[15] 杜海顺, 张旭东, 金勇, 等. 基于Gabor低秩恢复稀疏表示分类的人脸图像识别方法[J]. 电子学报, 2014, 42(12):2386-2393 DU Haishun, ZHANG Xudong, JIN Yong, et al. Face image recognition method via Gabor low-rank recovery sparse representation-based classification[J]. Acta electronica sinica, 2014, 42(12):2386-2393
[16] FIELD D J. Relations between the statistics of natural images and the response properties of cortical cells[J]. Journal of the optical society of America A, 1987, 4(12):2379-2394.
[17] 刘元, 吴小俊. 基于Log-Gabor滤波与黎曼流形学习的图像识别算法[J]. 模式识别与人工智能, 2015, 28(10):946-952 LIU Yuan, WU Xiaojun. Image recognition algorithm based on Log-Gabor wavelet and Riemannian manifold Learning[J]. Pattern recognition and artificial intelligence, 2015, 28(10):946-952
[18] CHEN XI, ZHANG Jiashu. Illumination robust single sample face recognition using multi-directional orthogonal gradient phase faces[J]. Neurocomputing, 2011, 74(14/15):2291-2298.
[19] 杨恢先, 蔡勇勇, 翟云龙, 等. 基于正交梯度二值模式的单样本人脸识别[J]. 计算机应用, 2014, 34(2):546-549 YANG Huixian, CAI Yongyong, ZHAI Yunlong, et al. Single sample face recognition based on orthogonal gradient binary pattern[J]. Journal of computer applications, 2014, 34(2):546-549
[20] 杨恢先, 翟云龙, 蔡勇勇, 等. 基于中心对称梯度幅值相位模式的单样本人脸识别[J]. 光电子·激光, 2015, 26(5):969-977 YANG Huixian, ZHAI Yunlong, CAI Yongyong, et al. Face recognition based on center-symmetric gradient magnitude and phase patterns[J]. Journal of optoelectronics·laser, 2015, 26(5):969-977
- 相似文献/References:
-
[1]金一,阮秋琦.一种局部加权的二维主成分分析算法及其在人脸识别中的应用[J].智能系统学报,2007,2(3):25.
JIN Yi,RUAN Qiu-qi.A partially weighted twodimensional PCA for face recognition[J].CAAI Transactions on Intelligent Systems,2007,2():25.
[2]任小龙,苏光大,相 燕.使用第2代身份证的人脸识别身份认证系统[J].智能系统学报,2009,4(3):213.
REN Xiao-long,SU Guang-da,XIANG Yan.Face authentication system using the Chinese second generation identity card[J].CAAI Transactions on Intelligent Systems,2009,4():213.
[3]苏光大.多技术合力的人脸识别系统设计[J].智能系统学报,2009,4(6):471.[doi:doi:10.3969/j.issn.1673-4785.2009.06.001]
SU Guang-da.Face recognition system designed to integrate multiple techniques[J].CAAI Transactions on Intelligent Systems,2009,4():471.[doi:doi:10.3969/j.issn.1673-4785.2009.06.001]
[4]王科俊,邹国锋,张洁.SPCA参数对单样本人脸识别效果影响分析[J].智能系统学报,2011,6(6):531.
WANG Kejun,ZOU Guofeng,ZHANG Jie.Analysis of the influence of SPCA parameters on the recognition of a single sample face[J].CAAI Transactions on Intelligent Systems,2011,6():531.
[5]杜吉祥,翟传敏,叶永青.使用稀疏约束非负矩阵分解算法的跨年龄人脸识别[J].智能系统学报,2012,7(3):271.
DU Jixiang,ZHAI Chuanmin,YE Yongqing.An agespan face recognition method based on an NMF algorithm with sparseness constraints[J].CAAI Transactions on Intelligent Systems,2012,7():271.
[6]阮晓虎,李卫军,覃鸿,等.一种基于特征匹配的人脸配准判断方法[J].智能系统学报,2015,10(1):12.[doi:10.3969/j.issn.1673-4785.201312064]
RUAN Xiaohu,LI Weijun,QIN Hong,et al.An assessment method for face alignment based on feature matching[J].CAAI Transactions on Intelligent Systems,2015,10():12.[doi:10.3969/j.issn.1673-4785.201312064]
[7]孙劲光,孟凡宇.一种特征加权融合人脸识别方法[J].智能系统学报,2015,10(6):912.[doi:10.11992/tis.201509025]
SUN Jinguang,MENG Fanyu.Face recognition by weighted fusion of facial features[J].CAAI Transactions on Intelligent Systems,2015,10():912.[doi:10.11992/tis.201509025]
[8]马晓,张番栋,封举富.基于深度学习特征的稀疏表示的人脸识别方法[J].智能系统学报,2016,11(3):279.[doi:10.11992/tis.201603026]
MA Xiao,ZHANG Fandong,FENG Jufu.Sparse representation via deep learning features based face recognition method[J].CAAI Transactions on Intelligent Systems,2016,11():279.[doi:10.11992/tis.201603026]
[9]刘训利,龚勋,王国胤.一种基于非残差估计线性表示模型的人脸识别[J].智能系统学报,2014,9(3):285.[doi:10.3969/j.issn.1673-4785.201309065]
LIU Xunli,GONG Xun,WANG Guoyin.Face recognition based on the linear representation model without residual estimation[J].CAAI Transactions on Intelligent Systems,2014,9():285.[doi:10.3969/j.issn.1673-4785.201309065]
[10]夏洋洋,龚勋,洪西进.人脸识别背后的数据清理问题研究[J].智能系统学报,2017,12(5):616.[doi:10.11992/tis.201706025]
XIA Yangyang,GONG Xun,HONG Xijin.Research on the data cleansing problem for face recognition technology[J].CAAI Transactions on Intelligent Systems,2017,12():616.[doi:10.11992/tis.201706025]
备注/Memo
收稿日期:2017-08-17。
基金项目:湘潭大学博士启动基金项目（KZ07089）；湘潭大学校级科研项目（16XZX02）.
作者简介:杨恢先，男，1963年生，教授，硕士生导师，主要研究方向为图像处理、嵌入式系统。曾获湖南省科技厅科学技术进步奖三等奖1项，湖南省教育厅教学成果奖2项。获得国家发明专利5项，出版教材2部。;付宇，男，1993年生，硕士研究生，主要研究方向为图像处理和模式识别。;曾金芳，女，1978年生，讲师，博士，主要研究方向为智能信息处理和频谱校正。参与国家自然科学基金面上项目、湖南省自然科学基金等项目多项。
通讯作者:付宇.E-mail:292682322@qq.com
更新日期/Last Update:
2019-04-25