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一种基于QPSO优化的流形学习的视频人脸识别算法

刘宇琦 赵宏伟 王玉

刘宇琦, 赵宏伟, 王玉. 一种基于QPSO优化的流形学习的视频人脸识别算法. 自动化学报, 2020, 46(2): 256-263. doi: 10.16383/j.aas.c180359
引用本文: 刘宇琦, 赵宏伟, 王玉. 一种基于QPSO优化的流形学习的视频人脸识别算法. 自动化学报, 2020, 46(2): 256-263. doi: 10.16383/j.aas.c180359
LIU Yu-Qi, ZHAO Hong-Wei, WANG Yu. Video Face Recognition Method Based on QPSO and Manifold Learning. ACTA AUTOMATICA SINICA, 2020, 46(2): 256-263. doi: 10.16383/j.aas.c180359
Citation: LIU Yu-Qi, ZHAO Hong-Wei, WANG Yu. Video Face Recognition Method Based on QPSO and Manifold Learning. ACTA AUTOMATICA SINICA, 2020, 46(2): 256-263. doi: 10.16383/j.aas.c180359

一种基于QPSO优化的流形学习的视频人脸识别算法

doi: 10.16383/j.aas.c180359
基金项目: 

国家青年科学基金 61101155

吉林省优秀青年人才基金项目 20180520020JH

吉林省科技计划重点科技研发项目 20180201064SF

国家重点科技研发计划项目 2018YFC0830103

详细信息
    作者简介:

    刘宇琦   吉林大学计算机科学与技术学院博士生.主要研究方向为图像处理与模式识别. E-mail: liuyuq@jlu.edu.cn

    赵宏伟   吉林大学计算机科学与技术学院教授. 2001年获得吉林大学博士学位.主要研究方向为多媒体技术, 计算机图像处理, 智能控制与嵌入式系统.E-mail: zhaohw@jlu.edu.cn

    通讯作者:

    王玉   吉林大学应用技术学院副教授. 2017年获得吉林大学计算机科学与技术学院博士学位.主要研究方向为图像处理与机器学习.本文通信作者.E-mail: wangyu001@jlu.edu.cn

Video Face Recognition Method Based on QPSO and Manifold Learning

Funds: 

the National Science Foundation for Young Scientists of China 61101155

Outstanding Young Talent Foundation of Jilin Province 20180520020JH

Key Projects of Jilin Province Science and Technology Development Plan 20180201064SF

National Key and Development Plan 2018YFC0830103

More Information
    Author Bio:

    LIU Yu-Qi Ph. D. candidate at the College of Computer Science and Technology, Jilin University. His research interest covers image processing and pattern recognition

    ZHAO Hong-Wei Professor at the College of Computer Science and Technology, Jilin University. He received his Ph. D. degree from Jilin University in 2001. His research interest covers multimedia technology, computer image processing, intelligent control and embedded system

    Corresponding author: WANG Yu Associate professor at the College of Applied Technology, Jilin University. He received his Ph. D. degree from College of Computer Science and Technology, Jilin University in 2017. His research interest covers image processing and machine learning. Corresponding author of this paper
  • 摘要: 视频场景复杂多变, 视频采集设备不一致等原因, 导致无约束视频中充斥着大量的遮挡和人脸旋转, 视频人脸识别方法的准确率不高且性能不稳定.为解决上述问题, 本文提出了一种基于QPSO优化的流形学习的视频人脸识别算法.该算法将视频人脸识别视为图像集相似度度量问题, 首先帧图像对齐后提取纹理特征并进行融合, 再利用带有QPSO优化的黎曼流形大幅度简约维度以获得视频人脸的内在表示, 相似度则由凸包距离表示, 最后利用SVM分类器获得分类结果.通过在Youtube Face数据库和Honda/UCSD数据库上与当前主流算法进行的对比实验, 验证了本文算法的有效性, 所提算法识别精度较高, 误差较低, 并且对光照和表情变化具有较强的鲁棒性.
    Recommended by Associate Editor ZUO Wang-Meng
    1)  本文责任编委 左旺孟
  • 图  1  基于QPSO优化黎曼流形的视频人脸识别算法框架

    Fig.  1  The framework of proposed video face recognition algorithm based on manifold learning

    图  2  QPSO优化过程各参数系数变化

    Fig.  2  Optimization of parameters in QPSO optimization process

    图  3  QPSO优化过程中寻优适应度变化

    Fig.  3  Optimization of fitness in QPSO optimization process

    图  4  不同算法在Honda/UCSD上的CMC曲线

    Fig.  4  The CMC curves of different algorithms on Honda/UCSD database

    表  1  不同纹理描述算子在YouTube Face数据库上的识别率(%)

    Table  1  Recognition rate of different texture description operators on YouTube Face database (%)

    LBP CSLBP LBP CSLBP
    Method Acc±SE AUC EER Acc±SE AUC EER Acc±SE AUC EER Acc±SE AUC EER
    min dist 65.7±1.6 70.66 35.20 63.08±1.0 67.29 37.36 65.60±1.7 70.01 35.64 66.04±2.24 71.21 34.88
    max dist 57.90±1.7 61.06 42.64 56.46±2.2 58.80 43.76 55.70±2.4 58.10 45.32 57.44±2.21 59.91 43.20
    mean dist 63.72±2.2 68.34 36.84 61.10±2.1 64.86 39.52 62.86±1.4 66.98 38.20 63.88±2.18 67.88 37.20
    median dist 63.46±2.0 68.16 36.80 60.84±2.1 64.81 39.44 62.70±1.5 66.81 38.36 63.50±2.33 67.70 37.52
    mean min 65.12±1.7 69.99 35.84 62.62±1.5 66.48 38.28 65.48±1.8 69.22 36.56 65.48±2.15 70.04 35.96
    下载: 导出CSV

    表  2  不同算法在YouTube Face数据库上的识别率(%)

    Table  2  Recognition rate of different algorithm on YouTube Face database (%)

    With Logmap With MDS
    Method Acc±SE AUC EER Acc±SE AUC EER
    min dist 49.60±0.9 51.24 48.56 51.22±1.4 49.39 50.40
    max dist 50.00±0.2 50.71 49.96 50.20±2.0 50.74 49.56
    mean dist 50.16±0.6 50.64 49.48 49.64±1.2 50.09 50.68
    median dist 50.06±0.6 50.60 49.64 49.18±1.2 50.03 50.60
    mean min 50.18±0.7 50.43 49.48 50.16±0.7 49.55 50.68
    下载: 导出CSV

    表  3  黎曼流形在YouTube Face数据库上的识别率(%)

    Table  3  Recognition rate of manifold learning on YouTube Face database (%)

    With Logmap Without Logmap
    Method Acc±SE AUC EER Acc±SE AUC EER
    min dist 49.60±0.9 51.24 48.56 66.04±2.2 71.21 34.88
    max dist 50.00±0.2 50.71 49.96 57.44±2.2 59.91 43.20
    mean dist 50.16±0.6 50.64 49.48 63.88±2.1 67.88 37.20
    median dist 50.06±0.6 50.60 49.64 63.50±2.3 67.70 37.52
    mean min 50.18±0.7 50.43 49.48 65.48±2.1 70.04 35.96
    下载: 导出CSV

    表  4  不同算法在YouTube Face视频人脸数据库上的实验结果(%)

    Table  4  Recognition rate of different algorithms on YouTube Face database (%)

    CSLBP FPLBP LBP Fusion
    Method AUC EER AUC EER AUC EER AUC EER
    min dist 67.29 37.36 70.01 35.64 70.66 35.2 71.21 34.88
    max dist 58.8 43.76 58.1 45.32 61.06 42.64 59.91 43.2
    mean dist 64.86 39.52 66.98 38.2 68.34 36.84 67.88 37.2
    median dist 64.81 39.44 66.81 38.36 68.16 36.8 67.70 37.52
    most frontal 63.61 40.36 64.24 40.04 66.5 38.72 66.23 38.4
    nearest pose 63.24 40.32 64.35 40.2 66.87 37.88 66.29 38
    MSM 64.64 40.04 63.85 40.24 66.19 38.28 66.33 38.28
    CMSM 65.17 39.76 68.35 37.16 67.26 38.36 69.81 36.04
    $\left \|U_1^{\rm T}U_2\right \|$ 67.68 37.4 69.37 35.8 69.78 35.96 70.64 35.32
    Linear AHISD 60.06 42.32 60.14 42.28 64.55 39.24 64.71 39.28
    Kernel CHISD 66.65 38.6 67.01 38.56 68.89 37.2 68.35 37.4
    Proposed 67.52 30.55 74.21 29.55 79.43 28.34 77.35 32.02
    下载: 导出CSV

    表  5  不同算法在Honda/UCSD视频人脸数据库上的首选识别率

    Table  5  Recognition rate of different algorithms on Honda/UCSD database

    Algorithm Accuracy (%)
    ${GVLBP}_{1,4,1}+{1NN}$ 53.8
    ${LBP}$-${TOP}_{4,4,4,1,1,1} + {1NN}$ 53.8
    ${LBP}$-${TOP}_{8,8,8,1,1,1} + {1NN}$ 56.4
    ${GLBP}$-${TOP}_{8,8,8,1,1,1} + {1NN}$ 66.7
    ${GLBP}$-${TOP}_{8,8,8,1,1,1} + {1NN}$ 69.2
    The proposed 74.4
    下载: 导出CSV
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  • 收稿日期:  2018-05-29
  • 录用日期:  2018-10-06
  • 刊出日期:  2020-03-06

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