计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 126-132.doi: 10.11896/jsjkx.19050002
杨少鹏1, 刘宏哲1, 王雪峤2
YANG Shao-peng1, LIU Hong-zhe1, WANG Xue-qiao2
摘要: 人脸检测是指从输入图片或视频中找到人脸的精确位置并确定其大小。为了应对尺度多样性特别是小尺寸人脸给人脸检测任务带来的困难,文中提出一种新的基于特征图融合的小尺寸人脸检测方法。首先,合理地选择待检测特征图,使用不同的特征图检测不同大小的人脸。然后,通过将较深的特征图和较浅的特征图进行融合,合理地引入上下文信息,从而提高小尺寸人脸的检测精度。在NVIDIA GTX TATAN X上,使用WIDERFACE数据集对所提方法进行训练和测试,其在WIDERFACE 3个测试子集上的测试结果分别为88.9%(hard),93.5%(medium),94.3%(easy)AP,检测速度为39 fps,其检测精度和检测速度均优于其他优秀的检测方法。
中图分类号:
[1]JIA J.Research on Face Detection Technology Based on Deep Learning Network[J].China New Telecommunications,2017,19(24):54-55. [2]VIOLA P,JONES M.Rapid object detection using a boosted cascade of simple features[C]//IEEE Computer Society Confe-rence on Computer Vision & Pattern Recognition.Kauai,HI,USA,2001:511. [3]MATHIAS M,BENENSON R,PEDERSOLI M,et al.Face detection without bells and whistles[C]//European Conference on Computer Vision.Cham:Springer,2014:720-735. [4]LI H,LIN Z,SHEN X,et al.A convolutional neural network cascade for face detection[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2015:5325-5334. [5]WU S Z,KAN M,SHAN S G,et al.Funnel-structured cascade for multi-view face detection with alignment-awareness[J].Neurocomputing,2016,221(C):138-145. [6]YANG S,LUO P,CHEN C L,et al.Faceness-Net:Face detection through deep facial part responses[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,PP(99):1. [7]LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single shot multibox detector[C]//European Conference on Computer Vision.Amsterdam,Netherlands,2016:21-37. [8]ZHANG S,ZHU X,LEI Z,et al.S3FD:Single Shot Scale-invariant Face Detector[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:192-201. [9]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-time Object Detection with Region Proposal Networks[C]//Advances in Neural Information Processing Systems.2015:91-99. [10]REDMON J,DIVVALA S,GIRSHICK R,et al.You only Look Once:Unified,Real-time Object Detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:779-788. [11]ZHU C,ZHENG Y,LUU K,et al.CMS-RCNN:Con textual Multi-Scale Region-Based CNN for Unconstrained Face Detection [M]//Deep Learning for Biometrics.Cham:Springer,2017:57-79. [12]HU P,RAMANAN D.Finding Tiny Faces[C]// Computer Vision & Pattern Recognition.2017. [13]HE K,ZHANG X,REN S,et al.Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,37(9):1904-1916. [14]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-scale Image Recognition[J].arXiv:1409.1556. [15]LUO W,LI Y,URTASUN R,et al.Understanding the Effective Receptive Field in Deep Convolutional Neural Networks[C]//Advances in Neural Information Processing Systems.2016:4898-4906. [16]YANG S,LUO P,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. [17]CAO G,XIE X,YANG W,et al.Feature-Fused SSD:Fast Detection for Small Objects[C]//Ninth International Conference on Graphic and Image Processing (ICGIP 2017).International Society for Optics and Photonics,2018. [18]FU C Y,LIU W,RANGA A,et al.Dssd:Deconvolutional single shot detector[J].arXiv:1701.06659,2017. [19]SHRIVASTAVA A,GUPTA A,GIRSHICK R.Training region-based object detectors with online hard example mining[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:761-769. [20]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2980-2988. [21]ZHU C,TAO R,LUU K,et al.Seeing Small Faces from Robust Anchor’s Perspective[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:5127-5136. |
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