[1]于爱华,王洪安.基于特征优化与稀疏表示的3D掌纹分类[J].浙江科技学院学报,2017,(06):450-456.[doi:10.3969/j.issn.1671-8798.2017.06.009 ]
 YU Aihua,WANG Hongan.3D palm-print sparse representation classification based on optimized features[J].,2017,(06):450-456.[doi:10.3969/j.issn.1671-8798.2017.06.009 ]
点击复制

基于特征优化与稀疏表示的3D掌纹分类(/HTML)
分享到:

《浙江科技学院学报》[ISSN:1001-3733/CN:61-1062/R]

卷:
期数:
2017年06期
页码:
450-456
栏目:
出版日期:
2017-12-14

文章信息/Info

Title:
3D palm-print sparse representation classification based on optimized features
文章编号:
1671-8798(2017)06-0450-07
作者:
于爱华王洪安
浙江科技学院 自动化与电气工程学院,杭州 310023
Author(s):
YU Aihua WANG Hongan
School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China
关键词:
3D掌纹识别 压缩感知 投影矩阵优化 稀疏表示
分类号:
TP 391.41; TN911.7
DOI:
10.3969/j.issn.1671-8798.2017.06.009
文献标志码:
A
摘要:
针对大数据背景下3D掌纹技术存在的问题,提出一种基于优化投影矩阵的3D掌纹稀疏表示识别技术架构。系统首先提取3D掌纹表面类型特征,然后利用分块方向梯度直方图构成训练样本,通过优化设计投影矩阵,使得同类掌纹投影特征互相关性变大,异类掌纹投影特征互相关性变小; 最后利用投影后3D掌纹特征稀疏表示分类,并比较L0/L1/L2范数各种快速算法性能。通过投影优化后的系统,在识别率和实时性上都有所改善,仿真实验证实了研究工作的有效性。

参考文献/References:

[1] 李春燕,卢光明,黎伟.基于曲面曲率和RLDA的3D掌纹识别方法[J].中国图像图形学报,2011,16(5):807.
[2] 杨冰,王小华,杨鑫.基于局部纹理特征的三维掌纹识别研究[J].光电工程,2014,41(12):53.
[3] ZHANG L, SHEN Y, LI H Y, et al. 3D palmprint identification using block-wise features and collaborative representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(8):1730.
[4] LI W, ZHANG D, LU G, et al. A novel 3-D palmprint acquisition system[J]. IEEE Transactions Systems Man and Cybernetics,2012,42(2):443.
[5] TURK M, PENTLAND A. Eigen faces for recognition[J].Journal of Cognitive Neuroscience,1991,3(1):71.
[6] YU H, YANG J. A direct LDA algorithm for high-dimensional data: with application to face recognition[J]. Pattern Recognition,2001,34(10):2067.
[7] SWETS D L, WENG J. Using discriminate eigen features for image retrieval[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):831.
[8] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory,2006,52(4):1289.
[9] CANDES E J, WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine,2008,25(2):21.
[10] 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.
[11] YU A H, BAI H, JIANG Q R, et al. Multi-objects classification via optimized compressive sensing projection[C]//International Conference on Information, Communications and Signal Processing. Taibei: IEEE,2014:1.
[12] LI G, ZHU Z, YANG D. On projection matrix optimization for compressive sensing systems[J]. IEEE Transactions on Signal Processing,2013,61(11):2887.
[13] SNELICK R, ULUDAG U, MINK A, et al. Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):450.
[14] BESLAND P J, JAIN R C. Segmentation through variable-order surface fitting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1988,10(2):167.
[15] FLUSSER J, ZITOVA B, SUK T. Moments and moment invariant in pattern recognition[M]. Hoboken, New Jersey: Wiley Publishing,2009.
[16] YANG A Y, SASTRY S S, GANESH A, et al. Fast L1-minimization algorithms and an application in robust face recognition: a review[C]//IEEE International Conference on Image Processing. Las Vegas: CSREA Press,2010:1849.
[17] ASIF M S,ROMBERG J. Sparse signal recovery of streaming signals using L1-homotopy[J]. IEEE Transactions on Signal Processing,2013,62(16):4209.
[18] YANG J, ZHANG Y. Alternating direction algorithms for L1-problems in compressive sensing[J]. SIAM Journal on Scientific Computing,2011,33(1):250.
[19] BECK A, TEBOULLE M. A fast iterative shrinkage thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Science,2009,2(1):183.
[20] ZHANG L, YANG M, FENG X. Sparse representation or collaborative representation: which helps face recognition[C]//International Conference on Computer Vision. Washington: IEEE Computer Society,2011:471.
[21] ZELNIK M L, ROSENBLUM K,ELDAR Y C. Sensing matrix optimization for block-sparse decoding[J]. IEEE Transactions on Signal Processing,2011,59(9):4300.
[22] CLEJU N. Optimized projections for compressed sensing via rank-constrained nearest correlation matrix[J]. Applied and Computational Harmonic Analysis,2014,36(3):495.
[23] YU A H, BAI H, SUN B B, et al. Face recognition based on optimized projections for distributed intelligent monitoring systems[J]. International Journal of Distributed Sensor Networks,2016,10(4):1155.

备注/Memo

备注/Memo:
收稿日期: 2017-05-09
基金项目: 浙江省教育厅科研计划项目(Y201430687)
通信作者: 于爱华(1975— ),男,江苏省海安人,工程师,博士研究生,主要从事数字信号处理研究。E-mail:yuaihua_seu@163.com。
更新日期/Last Update: 1900-01-01