高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于单一神经网络的多尺度人脸检测

刘宏哲 杨少鹏 袁家政 王雪峤 薛建明

刘宏哲, 杨少鹏, 袁家政, 王雪峤, 薛建明. 基于单一神经网络的多尺度人脸检测[J]. 电子与信息学报, 2018, 40(11): 2598-2605. doi: 10.11999/JEIT180163
引用本文: 刘宏哲, 杨少鹏, 袁家政, 王雪峤, 薛建明. 基于单一神经网络的多尺度人脸检测[J]. 电子与信息学报, 2018, 40(11): 2598-2605. doi: 10.11999/JEIT180163
Hongzhe LIU, Shaopeng YANG, Jiazheng YUAN, Xueqiao WANG, Jianming XUE. Multi-scale Face Detection Based on Single Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2598-2605. doi: 10.11999/JEIT180163
Citation: Hongzhe LIU, Shaopeng YANG, Jiazheng YUAN, Xueqiao WANG, Jianming XUE. Multi-scale Face Detection Based on Single Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2598-2605. doi: 10.11999/JEIT180163

基于单一神经网络的多尺度人脸检测

doi: 10.11999/JEIT180163
基金项目: 国家自然科学基金(61571045),北京市属高校高水平教师队伍建设支持计划项目(IDHT20170511),国家科技支撑项目(2015BAH55F03),北京联合大学新起点项目(Zk10201703),北京市教委科技计划一般项目(KM201811417002)
详细信息
    作者简介:

    刘宏哲:女,1971年生,教授,硕士生导师,研究方向为数字图像处理、旅游信息化

    杨少鹏:男,1990年生,硕士生,研究方向为模式识别

    袁家政:男,1971年生,教授,博士生导师,研究方向为数字图像处理、视觉计算与定位技术

    王雪峤:女,1986年生,讲师,研究方向为模式识别

    薛建明:男,1992年生,硕士生,研究方向为模式识别

    通讯作者:

    杨少鹏  shaopeng568@163.com

  • 中图分类号: TP391.4

Multi-scale Face Detection Based on Single Neural Network

Funds: The National Natural Science Foundation of China (61571045), The Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (IDHT20170511), The National Science and Technology Support Project (2015BAH55F03), The Foundation of Beijing Union University (Zk10201703), The Foundation of Beijing Municipal Education Commission (KM201811417002)
  • 摘要: 人脸检测是指检测并定位输入图像中所有的人脸,并返回精确的人脸位置和大小,是目标检测的重要方向。为了解决人脸尺度多样性给人脸检测造成的困难,该文提出一种新的基于单一神经网络的特征图融合多尺度人脸检测算法。该算法在不同大小的卷积层上预测人脸,实现实时多尺度人脸检测,并通过将浅层的特征图融合引入上下文信息提高小尺寸人脸检测精度。在数据集FDDB和WIDERFACE测试结果表明,所提方法达到了先进人脸检测的水平,并且该方法去掉了框推荐过程,因此检测速度更快。在WIDERFACE难、适中、简单3个子数据集上测试结果分别为87.9%, 93.2%, 93.4% MAP,检测速度为35 fps。所提算法与目前效果较好的极小人脸检测方法相比,在保证精度的同时提高了人脸检测速度。
  • 图  1  SSD网络结构

    图  2  默认检测框[16]

    图  3  增加上下文信息[19]

    图  4  反卷积融合模块

    图  5  基于特征图融合的多尺度人脸检测网络结构

    图  6  特征图融合模型

    图  7  测试结果曲线

    图  8  FDDB上测试ROC曲线

    图  9  实验效果图

    表  1  检测框参数

    特征层 步长n 检测框大小 宽高比
    conv3_3 4 16 1
    conv4_3 8 32 1
    conv5_3 16 64 1
    conv7 32 128 1
    conv8_2 64 256 1
    conv9_2 128 512 1
    下载: 导出CSV

    表  2  不同融合方式的MAP对比结果

    模型名称 数据集 MAP
    本文的融合型 WIDER 0.879
    对比模型1 FACE 0.823
    对比模型2 (Hard) 0.836
    下载: 导出CSV

    表  3  实验结果MAP对比

    方法 适中 简单 检测速度(fps)
    Faster-rcnn 0.712 0.845 0.897 <10
    SSD-face 0.737 0.882 0.910 <43
    HR 0.831 0.914 0.925 <5
    本文方法 0.879 0.932 0.934 <35
    下载: 导出CSV
  • JIANG Huaizu and LEARNED M E. Face detection with the faster r-cnn[C]. IEEE International Conference on Automatic Face & Gesture Recognition, Washington, D.C., USA, 2017: 650–657.
    YANG Shuo, LUO Ping, LOY C, et al. WIDERFACE: A face detection benchmark[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 5525–5533.
    CROSSWHITE N, BYRNE J, STAUFFER C, et al. Template adaptation for face verification and identification[C]. IEEE International Conference on Automatic Face & Gesture Recognition, Washington, D.C., USA, 2017: 1–8.
    MAJUMDAR A, SINGH R, and VATSA M. Face verification via class sparsity based supervised encoding[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1273–1280 doi: 10.1109/TPAMI.2016.2569436
    GAO Yuan, MA Jiayi, and YUILLE A L. Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples[J]. IEEE Transactions on Image Processing, 2017, 26(5): 2545–2560 doi: 10.1109/TIP.2017.2675341
    HARIS KHAN M, MCDONAGH J, and TZIMIROPOULOS G. Synergy between face alignment and tracking via discriminative global consensus optimization[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, USA, 2017: 3791–3799.
    GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.
    VIOLA P and JONES M. Rapid object detection using a boosted cascade of simple features[C]. IEEE Computer Society Conference on Computer Vision & Pattern Recognition, Kauai, USA, 2001: 511.
    LI Jianguo, WANG Tao, and ZHANG Yimin. Face detection using SURF cascade[C]. IEEE International Conference on Computer Vision Workshops, Ontario, Canada, 2012: 2183–2190.
    MATHIAS M, BENENSON R, PEDERSOLI M, et al. Face detection without bells and whistles[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 720–735.
    LI Haoxiang, LIN Zhe, SHEN Xiaohui, et al. A convolutional neural network cascade for face detection[C]. Computer Vision and Pattern Recognition. Boston, USA, 2015: 5325–5334.
    WU Shuzhe, KAN M, SHAN Shiguang, et al. Funnel-structured cascade for multi-view face detection with alignment-awareness[J]. Neurocomputing, 2016, 221(C): 138–145.
    YANG Shuo, LUO Ping, CHEN C L, et al. Faceness-Net: Face detection through deep facial part responses[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(8): 1845–1859 doi: 10.1109/TPAMI.2017.2738644
    GIRSHICK R. Fast r-cnn[C]. Proceedings of The IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1440–1448.
    REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149 doi: 10.1109/TPAMI.2016.2577031
    LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]. European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 21–37.
    DAI Jifeng, LI Yi, HE Kaiming, et al. R-fcn: Object detection via region based fully convolutional networks[C]. Advances in Neural Information Processing Systems, Barcelona, Spain, 2016: 379–387.
    ZHU Chenchen, ZHENG Yutong, LUU K, et al. CMS-RCNN: Contextual multi-scale region-based CNN for unconstrained face detection[OL]. arXiv preprint arXiv:1606.05413, 2016.
    HU Peiyun and RAMANAN D. Finding tiny faces[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, 2017: 1522–1530.
    ERHAN D, SZEGEDY C, TOSHEV A, et al. Scalable object detection using deep neural networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 2147–2154.
    CHEN Chenyi, LIU Mingyu, TUZEL O, et al. R-cnn for small object detection[C]. Asian Conference on Computer Vision, Taipei, China, 2016: 214–230.
    BELL S, LAWRENCE ZITNICK C, BALA K, et al. Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 2874–2883.
    WONG R Y and HALL E L. Sequential hierarchical scene matching[J]. IEEE Transactions on Computers, 1978, 27(4): 359–366 doi: 10.1109/TC.1978.1675108
    FU C Y, LIU Wei, RANGA A, et al. DSSD: Deconvolutional single shot detector[OL]. arXiv preprint arXiv:1701.06659, 2017.
    WEI Xiang, ZHANG Dongqing, YU H, et al. Context-aware single-shot detector[C]. IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, USA, 2018: 1784–1793.
    HOWARD A G. Some improvements on deep convolutional neural network based image classification[OL]. arXiv preprint arXiv:1312.5402, 2013.
  • 加载中
图(9) / 表(3)
计量
  • 文章访问数:  1511
  • HTML全文浏览量:  904
  • PDF下载量:  87
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-02-07
  • 修回日期:  2018-07-05
  • 网络出版日期:  2018-07-23
  • 刊出日期:  2018-11-01

目录

    /

    返回文章
    返回