基于压缩感知的人脸识别方法

doi:10.3969/j.issn.1000-3428.2012.24.032

计算机工程 ›› 2012, Vol. 38 ›› Issue (24): 133-135. doi: 10.3969/j.issn.1000-3428.2012.24.032

基于压缩感知的人脸识别方法

邹伟1，李元祥1，杨俊杰1，周则明2

(1. 上海交通大学航空航天学院，上海 200240；2. 解放军理工大学气象学院，南京 211101)

收稿日期:2011-07-25 修回日期:2011-09-19 出版日期:2012-12-20 发布日期:2012-12-18
作者简介:邹伟(1986－)，男，硕士研究生，主研方向：模式识别，压缩感知；李元祥，副教授；杨俊杰，学士；周则明，教授
基金资助:
国家自然科学基金资助项目(41174164)

Face Recognition Method Based on Compressed Sensing

ZOU Wei1, LI Yuan-xiang1, YANG Jun-jie1, ZHOU Ze-ming2

(1. School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China;2. College of Meteorology, PLA University of Science and Technology, Nanjing 211101, China)

Received:2011-07-25 Revised:2011-09-19 Online:2012-12-20 Published:2012-12-18

摘要/Abstract

摘要： 基于稀疏重构的分类方法具有较好的识别效果，但计算复杂度高。为此，提出基于压缩感知的人脸识别方法COMP，将L1范数最小化重构算法替换成正交匹配追踪(OMP)算法，以降低复杂度，并在OMP中引入模式类别信息，使该方法具有更强的分类能力。基于YaleB人脸库的实验结果表明，COMP在低维度时识别率高于OMP。

关键词: 基于稀疏重构的分类方法, 稀疏重构, L1范数最小化, 正交匹配追踪算法, COMP方法

Abstract: Sparse Representation-based Classification(SRC) method performs excellent in face recognition but shows high complexity in computation. This paper proposes face recognition method based on Compressed Sensing(CS) named Classified Orthogonal Matching Pursuit(COMP). L1-norm minimization representation algorithm is replaced by Orthogonal Matching Pursuit(OMP) algorithm to reduce complexity, and mode category information is introduced in OMP to endow the method stronger ability to category. Experiments based on YaleB face database clarify that the recognition rate of COMP is higher than OMP.

Key words: Sparse Representation-based Classification(SRC) method, sparse representation, L1-norm minimization, Orthogonal Matching Pursuit(OMP) algorithm, Classified Orthogonal Matching Pursuit(COMP) method

中图分类号:

TP391

邹伟, 李元祥, 杨俊杰, 周则明. 基于压缩感知的人脸识别方法[J]. 计算机工程, 2012, 38(24): 133-135.

JU Wei, LI Yuan-Xiang, YANG Dun-Jie, ZHOU Ze-Meng. Face Recognition Method Based on Compressed Sensing[J]. Computer Engineering, 2012, 38(24): 133-135.

https://www.ecice06.com/CN/Y2012/V38/I24/133

参考文献

[1] Kashin B. The Widths of Certain Finite Dimensional Sets and Classes of Smooth Functions[J]. Izvestiya Rossiiskoi Akademii Nauk. Seriya Matematicheskaya, 1977, 41(2): 334-351.
[2] Donoho D L. Compressed Sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[3] Candès E J, Romberg J, Tao Terence. Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
[4] Rauhut H, Schass K, Vandergheynst P. Compressed Sensing and Redundant Dictionaries[J]. IEEE Transactions on Information Theory, 2008, 54(5): 2210-2219.
[5] Baraniuk R, Steeghs P. Compressive Radar Imaging[C]//Proc. of IEEE Radar Conference. Houston, USA: IEEE Press, 2007: 128- 133.
[6] Duarte M, Davenport M, Takbar D, et al. Single-pixel Imaging via Compressive Sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 82-91.
[7] Sheikh M, Milenkovic O, Baraniuk R. Designing Compressive Sensing DNA Microarrays[C]//Proc. of CAMPSAP’07. Washington D. C., USA: IEEE Press, 2007: 141-144.
[8] Borgnat P, Flandrin P. Time-frequency Localization from Sparsity Constraints[C]//Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing. Piscataway, USA: IEEE Press, 2008: 3785-3788.
[9] Willett R, Gehm M, Brady D. Multiscale Reconstruction for Computational Spectral Imaging[EB/OL]. [2011-04-12]. http:// people.ee.duke.edu/~willett/papers/WillettGehmBrady_SpectralImCS.pdf.
[10] Ma Jianwei, le Dimet F X. Deblurring from Highly Incomplete Measurements for Remote Sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3): 792-802.
[11] Lustig M, Donoho D L, Pauly J M. Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging[J]. Magnetic Resonance in Medicine, 2007, 58(6): 1182-1195.
[12] Shamsi D, Boufounos P, Koushanfar F. Noninvasive Leakage Power Tomography of Integrated Circuits by Compressive Sensing[C]//Proc. of the 13th International Symposium on Low Power Electronics and Design. Bangalore, India: [s. n.], 2008: 341- 346.
[13] Liu Danhua, Shi Guangming, Gao Dahua. A New Method for Signal Sparse Decomposition[C]//Proc. of Int’l Symposium on Intelligent Signal Processing and Communications Systems. [S. l.]: IEEE Press, 2007: 45-48.
[14] Shi Guangming, Lin Jie, Chen Xuyang, et al. UWB Echo Signal Detection with Ultra-low Rate Sampling Based on Compressed Sensing[J]. IEEE Transactions on Circuits and Systems II, 2008, 55(4): 379-383.
[15] Wang Liangjun, Wu Xiaolin, Shi Guangming. A Compressive Sensing Approach of Multiple Descriptions for Network Multimedia Communication[C]//Proc. of the 10th International Workshop on Multimedia Signal Processing. [S. l.]: IEEE Press, 2008: 445-449.
[16] 石光明, 刘丹华, 高大化, 等. 压缩感知理论及其研究进展[J]. 电子学报, 2009, 37(5): 1070-1081.
[17] 刘丹华, 石光明, 周佳社, 等. 基于Compressed Sensing 框架的图像多描述编码方法[J]. 红外与毫米波学报, 2009, 28(4): 298- 302.
[18] Zhang Yifu, Mei Shunliang, Chen Quqing, et al. A Novel Image/ Video Coding Method Based on Compressed Sensing Theory[C]// Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing. [S. l.]: IEEE Press, 2008: 1361-1364.
[19] 尹忠科, 解梅, 王建英. 基于稀疏分解的图像去噪[J]. 电子科技大学学报, 2006, 35(6): 876-878.
[20] 傅霆, 尧德中. 稀疏分解的加权迭代方法及其初步应用[J]. 电子学报, 2004, 32(4): 567-570.
[21] 李小波. 基于压缩感知的测量矩阵研究[D]. 北京: 北京交通大学, 2010.
[22] 乔雅莉. 基于稀疏表示的图像去噪算法研究[D]. 北京: 北京交通大学, 2009.
[23] 高睿. 基于压缩传感的匹配追踪重建算法研究[D]. 北京: 北京交通大学, 2009.
[24] 周灿梅. 基于压缩感知的信号重建算法研究[D]. 北京: 北京交通大学, 2010.
[25] Wright J, Yang Allen, Ganesh A, et al. Robust Face Recognition via Sparse Representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
[26] 高敏. 基于CS的SAR目标识别[D]. 西安: 西安电子科技大学, 2010.
[27] 杨荣根, 任明武, 杨静宇. 基于稀疏表示的人脸识别方法[J]. 计算机科学, 2010, 37(9): 267-269.
[28] Turk M, Pentland A. Eigenfaces for Recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86.
[29] 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.

选择文件类型/文献管理软件名称

选择包含的内容

基于压缩感知的人脸识别方法

Face Recognition Method Based on Compressed Sensing

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价

[1]	王淼, 蔡晓霞, 雷迎科. 基于改进时频检测的欠定变速跳频信号盲分离算法[J]. 计算机工程, 2020, 46(8): 172-177,183.
[2]	李虎,徐岩. 基于DSKSVD字典学习的语音信号欠定盲源分离算法[J]. 计算机工程, 2018, 44(10): 252-257.
[3]	黄志清,张严心,李梦佳,成志鹏. 基于StOMP算法的WSN压缩感知数据重构[J]. 计算机工程, 2017, 43(9): 149-155.
[4]	刘庆华,周秀清. 稀疏互质阵列的迭代加权l1范数约束来波方向估计[J]. 计算机工程, 2017, 43(4): 110-115.
[5]	杨爱萍,田玉针,何宇清,董翠翠. 基于改进K-SVD 和非局部正则化的图像去噪[J]. 计算机工程, 2015, 41(5): 249-253.
[6]	臧飞,杨沁梅. 基于稀疏邻域的特征融合算法及其应用[J]. 计算机工程, 2014, 40(8): 163-167.
[7]	郑豪, 金忠. 一种有监督的稀疏保持近邻嵌入算法[J]. 计算机工程, 2011, 37(16): 155-157.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于压缩感知的人脸识别方法

Face Recognition Method Based on Compressed Sensing

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价