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基于优化LeNet-5的近红外图像中的静默活体人脸检测

黄俊 张娜娜 章惠

黄俊, 张娜娜, 章惠. 基于优化LeNet-5的近红外图像中的静默活体人脸检测[J]. 红外技术, 2021, 43(9): 845-851.
引用本文: 黄俊, 张娜娜, 章惠. 基于优化LeNet-5的近红外图像中的静默活体人脸检测[J]. 红外技术, 2021, 43(9): 845-851.
HUANG Jun, ZHANG Nana, ZHANG Hui. Silent Live Face Detection in Near-Infrared Images Based on Optimized LeNet-5[J]. Infrared Technology , 2021, 43(9): 845-851.
Citation: HUANG Jun, ZHANG Nana, ZHANG Hui. Silent Live Face Detection in Near-Infrared Images Based on Optimized LeNet-5[J]. Infrared Technology , 2021, 43(9): 845-851.

基于优化LeNet-5的近红外图像中的静默活体人脸检测

基金项目: 

上海市教育委员会“晨光计划”基金项目 AASH1702

详细信息
    作者简介:

    黄俊(1996-), 男, 浙江温州人, 硕士研究生, 主要研究方向:图像处理、计算机视觉。E-mail:huangj_sg@163.com

    通讯作者:

    张娜娜(1979-), 女, 山东莱阳人, 副教授, 硕士, 主要研究方向:图像处理。E-mail:nanazhang2004@163.com

  • 中图分类号: TP399

Silent Live Face Detection in Near-Infrared Images Based on Optimized LeNet-5

  • 摘要: 针对当前交互式活体检测过程繁琐、用户体验性差的问题,提出了一种优化LeNet-5和近红外图像的静默活体检测方法。首先,采用近红外光摄像头构建了一个非活体数据集;其次,通过增大卷积核、增加卷积核数目、引入全局平均池化等方法对LeNet-5进行了优化,构建了一个深层卷积神经网络;最后,将近红外人脸图片输入到模型中实现活体静默活体检测。实验结果表明,所设计的模型在活体检测数据集上有较高的识别率,为99.95%,整个静默活体检测系统的运行速度约为18~22帧/s,在实际应用中鲁棒性较高。
  • 图  1  LeNet_Liveness结构图

    Figure  1.  LeNet_Liveness structure diagram

    图  2  近红外活体检测数据示例

    Figure  2.  Examples of near-infrared liveness detection data

    图  3  数据训练过程

    Figure  3.  Data training process

    图  4  卷积层相关特征

    Figure  4.  Convolution layer related features

    图  5  活体检测系统示例

    注:系统测试对象均未在数据集中出现过

    Figure  5.  Examples of live detection systems

    Note: None of the system test objects have appeared in the dataset

    表  1  模型结构参数

    Table  1.   Model structure parameters

    Layer Name Layer Type Output Size/Strides Kernel Size
    Input Input layer 128×128×3/- -
    C1 Convolution 128×128×32/1 7
    P1 Max Pooling 64×64×32/2 2
    C2 Convolution 64×64×64/1 7
    P2 Max Pooling 32×32×64/2 2
    C3 Convolution 32×32×128/1 5
    P3 Max Pooling 16×16×128/2 2
    C4 Convolution 16×16×256/1 5
    P4 Max Pooling 8×8×256/2 2
    C5 Convolution 8×8×512/1 5
    P5 Max Pooling 4×4×512/2 2
    GAP GAP 1×1×512/1 4
    Softmax Softmax 2/- -
    下载: 导出CSV

    表  2  10折交叉验证结果

    Table  2.   10-fold cross-validation results

    Category Test Dadaset
    1 2 3 4 5 6 7 8 9 10
    Liveness 99.97 99.97 99.97 100 100 99.97 99.97 99.97 100 99.95
    Non-liveness 99.98 99.95 99.91 99.98 99.88 99.93 99.86 99.93 99.91 99.83
    Overall 99.96 99.95 99.94 99.95 99.98 99.95 99.91 99.95 99.96 99.90
    下载: 导出CSV

    表  3  三种算法结果比较

    Table  3.   Comparison of the results of the three algorithms

    Algorithm Accuracy/% Average prediction time for a single picture/ms
    GPU CPU
    SVM 96.67 - 4.43
    LeNet-5 98.23 2.03 7.57
    LeNet_Liveness 99.95 10.77 31.08
    下载: 导出CSV

    表  4  不同文献结果比较

    Table  4.   Comparison of results from different literature

    Detection type Literature Algorithm Equipment Accuracy/%
    Interactive [2] Head posture + mouth opening and closing detection Visible light camera 99.25
    [4] Random emoji commands Visible light camera 95.85
    [5] Blink detection + smile detection + open mouth detection VTM camera 97.67
    Silent [13] LBP+Gabor+SVM Visible light camera 98.00
    [14] SVM+3D point cloud reconstruction+Face key point Binocular camera(Near infrared light+visible light) 99.00
    [15] CNN(double-mean pooling +multiple types of activation function) Visible light camera 99.67
    This article CNN (LeNet-5 improvements) near-infrared camera(Near infrared light) 99.95
    下载: 导出CSV
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  • 被引次数: 0
出版历程
  • 收稿日期:  2020-12-01
  • 修回日期:  2021-01-20
  • 刊出日期:  2021-09-20

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