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弱对齐的跨光谱人脸检测

闫梦凯 钱建军 杨健

闫梦凯, 钱建军, 杨健. 弱对齐的跨光谱人脸检测. 自动化学报, 2021, x(x): 1−13 doi: 10.16383/j.aas.c210058
引用本文: 闫梦凯, 钱建军, 杨健. 弱对齐的跨光谱人脸检测. 自动化学报, 2021, x(x): 1−13 doi: 10.16383/j.aas.c210058
Yan Meng-Kai, Qian Jian-Jun, Yang Jian. Weakly aligned cross-spectral face detection. Acta Automatica Sinica, 2021, x(x): 1−13 doi: 10.16383/j.aas.c210058
Citation: Yan Meng-Kai, Qian Jian-Jun, Yang Jian. Weakly aligned cross-spectral face detection. Acta Automatica Sinica, 2021, x(x): 1−13 doi: 10.16383/j.aas.c210058

弱对齐的跨光谱人脸检测

doi: 10.16383/j.aas.c210058
基金项目: 国家自然科学基金(61876083), 国家自然科学基金联合基金(U1713208)资助
详细信息
    作者简介:

    闫梦凯:南京理工大学计算机科学与工程学院博士研究生, 主要研究方向为生物生理信息测量, 计算机视觉. E-mail: ymk@njust.edu.cn

    钱建军:南京理工大学计算机科学与工程学院副教授. 2014年获得南京理工大学博士学位. 主要研究方向为模式识别, 计算机视觉. 本文通信作者. E-mail: csjqian@njust.edu.cn

    杨健:南京理工大学计算机科学与工程学院教授. 2002年获得南京理工大学博士学位. 主要研究方向为模式识别, 计算机视觉, 机器学习. E-mail: csjyang@njust.edu.cn

Weakly Aligned Cross-spectral Face Detection

Funds: Supported by National Natural Science Foundation of P. R. China (61876083), and National Science Fund of China under Grant(U1713208)
More Information
    Author Bio:

    YAN Meng-Kai Ph. D. candidate at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. His research interest covers Biophysiological information measurement and computer vision

    QIAN Jian-Jun Associate Professor at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. He received his Ph. D. degree from Nanjing University of Science and Technology, China in 2014. His research interest covers pattern recognition and computer vision. Corresponding author of this paper

    YANG Jian Professor at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. He received his Ph. D. degree from Nanjing University of Science and Technology, China in 2002. His research interest covers pattern recognition, computer vision and machine learning

  • 摘要: 跨光谱人脸检测在活体人脸识别、体温筛查等领域有着重要的应用价值. 众所周知, 可见光人脸易于检测, 然而红外人脸难于检测, 因此借助可见光图像的人脸检测结果进而完成红外人脸检测是一种有效的解决方案. 但是跨光谱图像之间不可避免的存在偏差, 导致检测精度不高. 为了解决这一问题, 本文提出了一种弱对齐跨光谱图像的人脸检测算法, 该方法基于跨光谱图像之间的偏差设计了候选框布置策略, 并在此基础上提出了跨光谱特征表示方法用于选取最优候选框. 此外, 本文还构建了一个跨光谱人脸数据集(Cross-spectrum face简称为CSF). 最后, 在CSF和OTCBVS (OTCBVS Benchmark dataset collection)人脸数据集上的实验结果证明, 本文的方法能够较好地完成红外图像人脸检测任务.
  • 图  1  跨光谱人脸检测

    Fig.  1  Cross-spectral face detection

    图  2  双相机与空间内任意一点的关系

    Fig.  2  The relationship between dual cameras and any point in space

    图  3  空间中任意一点在相机中的成像坐标

    Fig.  3  The imaging coordinates of any point in space in the camera

    图  4  像素坐标系与图像坐标系的关系

    Fig.  4  The relationship between pixel coordinate system and image coordinate system

    图  5  不同深度下的跨光谱人脸图像

    Fig.  5  Cross-spectral face images at different depths

    图  6  含有运动目标的跨光谱人脸图像

    Fig.  6  Cross-spectral face images with moving target

    图  7  人脸高度与其成像高度的关系

    Fig.  7  Relationship between face height and image height

    图  8  跨光谱人脸检测框架

    Fig.  8  Cross-spectral face detection framework

    图  9  跨光谱特征表示网络

    Fig.  9  Cross-spectral feature representation network

    图  10  跨光谱特征表示网络训练方式

    Fig.  10  Cross-spectral feature representation network training method

    图  11  含有部分人脸的负样本

    Fig.  11  Improved negative sample selection method

    图  12  相机安装位置

    Fig.  12  Camera installation location

    图  13  不同采集条件下的图像

    Fig.  13  Images under different acquisition conditions

    图  14  检测结果对比图

    Fig.  14  Comparison of face detection results

    表  1  测试集为CSF-白天的实验结果

    Table  1  The experment results on CSF-day

    算法AP(%) Iou>0.5AP(%) Iou>0.3
    坐标映射44.688.4
    粗略纠正55.987.9
    本文算法87.589.6
    下载: 导出CSV

    表  2  测试集为CSF-夜间的实验结果

    Table  2  The experment results on CSF- night

    算法AP(%) Iou>0.5AP(%) Iou>0.3
    坐标映射36.982.7
    粗略纠正50.882.4
    本文算法81.884.1
    下载: 导出CSV

    表  3  测试集为OTCBVS的实验结果

    Table  3  The experment results on OTCBVS

    算法AP(%) Iou>0.5AP(%) Iou>0.3
    坐标映射16.446.8
    粗略纠正54.576.5
    本文算法74.486.6
    下载: 导出CSV

    表  4  候选框召回率

    Table  4  Proposal recall(%)

    数据集Iou>0.5Iou>0.3
    CSF96.998.4
    OTCBVS89.391.6
    下载: 导出CSV

    表  5  CSF中候选框的选取对模型的影响

    Table  5  The influence of the selection of the proposal on the model in CSF

    候选框AP(Iou>0.5)时间(ms)
    1/871.49
    1/8,2/884.816
    1/8,2/8,3/886.323
    1/8,…,4/886.428
    下载: 导出CSV

    表  6  OTCBVS中候选框的选取对模型的影响

    Table  6  The influence of the selection of the proposal on the model in OTCBVS

    候选框AP(Iou>0.5)时间(ms)
    1/870.510
    1/8,2/873.116
    1/8,2/8,3/874.224
    1/8,…,4/874.430
    下载: 导出CSV

    表  7  负样本类型对模型精度的影响

    Table  7  Effect of negative sample type on model accuracy

    负样本类型AP(%) Iou>0.5
    046.1
    4/870.5
    5/869.7
    6/847.1
    7/821.5
    0,4/8,5/8,6/8,7/886.4
    下载: 导出CSV

    表  8  CSF数据集上的对比实验结果

    Table  8  Comparative experiment results on CSF dataset

    算法AP(%) Iou>0.5AP(%) Iou>0.3
    FaceBoxes9.19.1
    S3FD9.19.1
    Pyramidbox35.936.3
    DSFD35.836.3
    Tinyface56.582.4
    S3FD-IR72.373.4
    DSFD-IR81.983.7
    DSFD-本文算法86.488.5
    下载: 导出CSV

    表  9  OTCBVS数据集上的对比实验结果

    Table  9  Comparative experiment results on OTCBVS dataset

    算法AP(%) Iou > 0.5AP(%) Iou > 0.3
    FaceBoxes--
    S3FD9.19.1
    Pyramidbox36.136.1
    DSFD27.227.2
    Tinyface25.138.6
    S3FD-IR60.873.6
    DSFD-IR69.470.4
    DSFD-本文算法75.086.3
    下载: 导出CSV
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  • 收稿日期:  2021-01-20
  • 修回日期:  2021-05-28
  • 网络出版日期:  2021-07-19

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