[1]狄岚,矫慧文,梁久祯.稀疏综合字典学习的小样本人脸识别[J].智能系统学报,2021,16(2):218-227.[doi:10.11992/tis.201910028]
DI Lan,JIAO Huiwen,LIANG Jiuzhen.Sparse comprehensive dictionary learning for small-sample face recognition[J].CAAI Transactions on Intelligent Systems,2021,16(2):218-227.[doi:10.11992/tis.201910028]
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DI Lan,JIAO Huiwen,LIANG Jiuzhen.Sparse comprehensive dictionary learning for small-sample face recognition[J].CAAI Transactions on Intelligent Systems,2021,16(2):218-227.[doi:10.11992/tis.201910028]
稀疏综合字典学习的小样本人脸识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第2期
页码:
218-227
栏目:
学术论文—机器学习
出版日期:
2021-03-05
- Title:
- Sparse comprehensive dictionary learning for small-sample face recognition
- Keywords:
- comprehensive dictionary learning; face recognition; class-specific dictionary learning; Fisher discrimination criterion; small sample; image expansion; mirror principle; extended interference dictionary; hybrid feature dictionary; low-rank dictionary
- 分类号:
- TP394
- 摘要:
- 传统以字典学习为基础的小样本人脸识别方法存在字典低辨别性、弱鲁棒性等缺点，对此，本文提出稀疏综合字典学习模型。该模型有效利用和生成人脸变化，以镜像原理及Fisher准则扩充训练样本多样性，通过构造混合特色字典、扩充干扰字典以及低秩字典原子，提取不同类别数据之间的共性、特殊性和异常情况，从而提高算法识别率以及对表情变化、姿态变化、遮挡等异常情况的处理能力。在AR、YALEB、LFW等人脸数据库进行仿真实验，实验结果验证了算法的有效性和可行性。
- Abstract:
- Traditional small-sample face recognition methods based on dictionary learning have disadvantages such as poor dictionary discrimination and lack of robustness. In this paper, we propose a sparse comprehensive dictionary learning model. This model effectively utilizes and generates facial changes, expands the diversity of training samples by the mirror principle and Fisher’s criterion, and extracts the commonalities, specialties, and anomalies between different categories of data by constructing a hybrid feature dictionary, extended interference dictionary, and low-rank dictionary atoms. This strategy improves the recognition rate of the algorithm and its ability to handle abnormal situations such as expression changes, pose changes, and occlusions. The results of simulation experiments performed on the face databases AR, YALEB, and LFW verify the effectiveness and feasibility of the proposed algorithm.
- 参考文献/References:
-
[1] OH B S, TOH K A, TEOH A B J, et al. An analytic Gabor feedforward network for single-sample and pose-invariant face recognition[J]. IEEE transactions on image processing, 2018, 27(6):2791-2805.
[2] ZHANG Ni, YAN Yan, CHEN Si, et al. Multi-label learning based deep transfer neural network for facial attribute classification[J]. Pattern recognition, 2018, 80:225-240.
[3] 冉鹏, 王灵, 李昕, 等. 改进Softmax分类器的深度卷积神经网络及其在人脸识别中的应用[J]. 上海大学学报(自然科学版), 2018, 24(3):352-366
RAN Peng, WANG Ling, LI Xin, et al. Improved Softmax classifier for deep convolution neural networks and its application in face recognition[J]. Journal of Shanghai University (Natural Science), 2018, 24(3):352-366
[4] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE transactions on signal processing, 2006, 54(11):4311-4322.
[5] YANG Meng, CHANG Heyou, LUO Weixin. Discriminative analysis-synthesis dictionary learning for image classification[J]. Neurocomputing, 2017, 219:404-411.
[6] SHEKHAR S, PATEL V M, CHELLAPPA R. Analysis sparse coding models for image-based classification[C]//Proceedings of 2014 IEEE International Conference on Image Processing. Paris, France, 2015.
[7] WU Fei, JING Xiaoyuan, YOU Xinge, et al. Multi-view low-rank dictionary learning for image classification[J]. Pattern recognition, 2016, 50:143-154.
[8] ZHANG Qiang, LI Baoxin. Discriminative K-SVD for dictionary learning in face recognition[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA, 2010.
[9] JIANG Zhuolin, LIN Zhe, DAVIS L S. Label consistent K-SVD:learning a discriminative dictionary for recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(11):2651-2664.
[10] MAIRAL J, BACH F, PONCE J, et al. Discriminative learned dictionaries for local image analysis[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA, 2008.
[11] CASTRODAD A, SAPIRO G. Sparse modeling of human actions from motion imagery[J]. International journal of computer vision, 2012, 100(1):1-15.
[12] 王念兵, 吴秦, 梁久祯, 等. 变化字典学习与显著特征提取的单样本人脸识别[J]. 小型微型计算机系统, 2017(9)
WANG Nian-bing, WU Qin, LIANG Jiu-zhen, XU Jie, ZHANG Huai. Single Sample Face Recognition via Variant Dictionary Learning and Salient Feature Extraction[J]. Journal of Chinese Computer Systems, 2017(9)
[13] YANG Meng, ZHANG Lei, FENG Xiangchu, et al. Sparse representation based fisher discrimination dictionary learning for image classification[J]. International journal of computer vision, 2014, 109(3):209-232.
[14] 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.
[15] SPRECHMANN P, SAPIRO G. Dictionary learning and sparse coding for unsupervised clustering[C]//Proceedings of 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, TX, USA, 2010:2042-2045.
[16] RAMIREZ I, SPRECHMANN P, SAPIRO G. Classification and clustering via dictionary learning with structured incoherence and shared features[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA, 2010.
[17] YANG Meng, ZHANG Lei, FENG Xiangchu, et al. Fisher discrimination dictionary learning for sparse representation[C]//Proceedings of 2011 IEEE International Conference on Computer Vision (ICCV). Barcelona, Spain, 2011:543-550.
[18] 王威, 朱宗玖, 陆俊. 基于字典学习和局部约束的稀疏表示人脸识别[J]. 电脑知识与技术, 2018, 14(5):200-202
WANG Wei, ZHU Zongjiu, LU Jun. Sparse representation for face recognition based on dictionary learning and locality constraint[J]. Computer knowledge and technology, 2018, 14(5):200-202
[19] 黎明, 彭秀姣, 王艳. 基于改进的字典学习与稀疏表示的人脸表情识别[J]. 系统仿真学报, 2018, 30(1):28-35, 44
LI Ming, PENG Xiujiao, WANG Yan. Facial expression recognition based on improved dictionary learning and sparse representation[J]. Journal of system simulation, 2018, 30(1):28-35, 44
[20] DENG Weihong, HU Jiani, GUO Jun. Extended SRC:undersampled face recognition via intraclass variant dictionary[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(9):1864-1870.
[21] LI Wenjing, LIANG Jiuzhen, WU Qin, et al. An efficient face classification method based on shared and class-specific dictionary learning[C]//Proceedings of 2015 IEEE International Conference on Image Processing (ICIP). Quebec City, QC, Canada, 2015.
[22] LI Wenjing, LIANG Jiuzhen. Adaptive face representation via class-specific and intra-class variation dictionaries for recognition[J]. Multimedia tools and applications, 2018, 77(12):14783-14802.
[23] NEEDELL D, TROPP J A. CoSaMP:iterative signal recovery from incomplete and inaccurate samples[J]. Applied and computational harmonic analysis, 2009, 26(3):301-321.
[24] DO T T, GAN Lu, NGUYEN N, et al. Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]//Proceedings of the 42nd Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, USA, 2009.
[25] TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the royal statistical society:series B (Methodological), 1996, 58(1):267-288.
[26] EFRON B, HASTIE T, JOHNSTONE I, et al. Least angle regression[J]. Annals of statistics, 2004, 32(2):407-499.
[27] MAIRAL J, BACH F, PONCE J. Task-driven dictionary learning[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(4):791-804.
[28] PENG Yigang, GANESH A, WRIGHT J, et al. RASL:robust alignment by sparse and low-rank decomposition for linearly correlated images[J]. IEEE transactions on pattern analysis and machine intelligence, 2012, 34(11):2233-2246.
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备注/Memo
收稿日期:2019-10-23。
基金项目:江苏省研究生科研与实践创新计划项目（KYCX19_1895）；道路交通安全公安部重点实验室开放课题（2020ZDSYSKFKT03-2，A类）
作者简介:狄岚，副教授，中国人工智能学会粒计算与知识发现专业委员会委员，中国计算机学会会员，江苏省“六大人才高峰”资助对象，主要研究方向为数字图像处理和计算机仿真。近几年先后主持及参与国家级、省部级科研项目7项，主持校级科研项目4项、企业合作项目近20项，获省级自然科学学术奖1次，行业联合会技术奖3次。发表学术论文50余篇;矫慧文，硕士研究生，中国计算机学会会员，主要研究方向为图像处理和人脸识别;梁久祯，教授，博士，中国计算机学会会员，主要研究方向为计算机视觉和数字图像处理。主持项目10余项，获得浙江省青年英才奖等。发表教材与专著4部，专利成果57项，发表学术论文160余篇
通讯作者:狄岚.E-mail:dilan@jiangnan.edu.cn
更新日期/Last Update:
2021-04-25