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基于块对角投影表示的人脸识别

刘保龙 王勇 李丹萍 王磊

刘保龙, 王勇, 李丹萍, 等 . 基于块对角投影表示的人脸识别[J]. 北京航空航天大学学报, 2021, 47(3): 623-631. doi: 10.13700/j.bh.1001-5965.2020.0460
引用本文: 刘保龙, 王勇, 李丹萍, 等 . 基于块对角投影表示的人脸识别[J]. 北京航空航天大学学报, 2021, 47(3): 623-631. doi: 10.13700/j.bh.1001-5965.2020.0460
LIU Baolong, WANG Yong, LI Danping, et al. Block-diagonal projective representation for face recognition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 623-631. doi: 10.13700/j.bh.1001-5965.2020.0460(in Chinese)
Citation: LIU Baolong, WANG Yong, LI Danping, et al. Block-diagonal projective representation for face recognition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 623-631. doi: 10.13700/j.bh.1001-5965.2020.0460(in Chinese)

基于块对角投影表示的人脸识别

doi: 10.13700/j.bh.1001-5965.2020.0460
基金项目: 

国家重点研发计划 2016YFE0207000

国家自然科学基金 61203137

国家自然科学基金 61401328

陕西省自然科学基础研究计划 2014JQ8306

陕西省自然科学基础研究计划 2015JM6279

详细信息
    作者简介:

    刘保龙  男,硕士研究生。主要研究方向:机器学习、模式识别

    王磊  男,博士,副教授,硕士生导师。主要研究方向:模式识别与分类、机器学习、计算机视觉等

    通讯作者:

    王磊, E-mail: leiwang@mail.xidian.edu.cn

  • 中图分类号: TP391

Block-diagonal projective representation for face recognition

Funds: 

National Key R & D Program of China 2016YFE0207000

National Natural Science Foundation of China 61203137

National Natural Science Foundation of China 61401328

Natural Science Basic Research Program of Shaanxi 2014JQ8306

Natural Science Basic Research Program of Shaanxi 2015JM6279

More Information
  • 摘要:

    针对大多数特征表示算法在挖掘高维数据内在结构时容易受到噪声的影响,以及特征学习与分类器设计割裂导致分类性能降低的问题,提出了一种新的基于特征表示的人脸识别方法,称为块对角投影表示(BDPR)学习。首先,利用样本信息对每类样本的编码系数施加一个加权矩阵,通过局部约束来加强表示系数之间的相似性,从而降低噪声对系数学习的影响,使所提方法能够更好地保持数据的局部结构。其次,为了实现数据与编码系数相关联,降低表示系数的学习难度,构造了块对角化判别约束项来学习一个判别投影,通过投影从低维数据中提取样本表示系数,使系数包含更多的样本间全局结构信息且具有更低的计算复杂度。最后,将系数学习和分类器学习整合到同一框架下,同时增大不同类别样本间的“标签距离”,采用迭代求解的方式交替更新判别投影和分类器,最终得到最适合当前表示特征的分类器,使得所提方法能自动完成分类。多个公开的人脸数据集上的实验结果表明:较之传统的协作表示分类和多个主流的子空间学习方法,所提方法均取得了更优的识别效果。

     

  • 图 1  CRC与BDPR在AR数据集上的样本编码系数对比

    Figure 1.  Comparison of sample coding coefficients learned by CRC and BDPR on AR dataset

    图 2  不同数据集上BDPR的函数收敛曲线

    Figure 2.  Function convergence curves of BDPR method on different datasets

    图 3  AR数据集上BDPR分类正确率随参数变化的三维柱状图

    Figure 3.  Three-dimensional histogram of classification accuracy of BDPR method changing with different parameters on AR dataset

    表  1  实验采用的数据集信息

    Table  1.   Information of dataset used in experiment

    数据集 类别 样本数 数据维度
    BANCA 52 520 2 576
    AR 50 1 300 2 200
    YaleB 38 2 414 1 024
    下载: 导出CSV

    表  2  各方法在不同数据集的最优分类正确率

    Table  2.   Highest classification accuracy of each method on different datasets   %

    方法 BANCA AR YaleB
    4 train 5 train 6 train 5 train 10 train 15 train 20 train 30 train 40 train
    CRC 46.03 45.92 47.74 68.79 81.85 87.58 92.67 88.69 96.02
    PCA 48.40 52.42 56.63 33.73 48.09 57.60 61.90 68.31 72.19
    LPP 41.12 38.78 42.84 38.00 48.44 61.20 83.97 88.69 90.83
    MFA 73.01 77.62 80.67 86.49 94.43 97.24 93.97 96.04 97.13
    CGDA 67.30 73.73 79.47 90.69 96.66 98.69 93.86 95.00 98.02
    RLSDP 68.97 76.38 82.12 88.96 96.69 98.69 91.90 95.49 96.86
    RLSL 72.18 77.50 82.13 84.97 95.63 97.87 93.47 96.85 98.15
    BDPR 75.80 79.88 84.38 93.01 97.38 98.60 94.33 97.37 98.48
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-25
  • 录用日期:  2020-09-04
  • 刊出日期:  2021-03-20

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