Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6): 126-132.doi: 10.11896/jsjkx.19050002

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Small Size Face Detection Based on Feature Map Fusion

YANG Shao-peng1, LIU Hong-zhe1, WANG Xue-qiao2   

  1. 1 Beijing Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China
    2 Institute of Computer Technology,Beijing Union University,Beijing 100101,China
  • Received:2019-05-06 Online:2020-06-15 Published:2020-06-10
  • About author:YANG Shao-peng,born in 1990,postgraduate.His main research interests include pattern recognition and so on.
    LIU Hong-zhe,born in 1971,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include pattern recognition and so on.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61871039,61802019,61906017),Beijing United University Lea-dership Program(BPHR2019AZ01),Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (IDHT20170511),National Science and Technology Support Project (2015BAH55F03),Foundation of Beijing Municipal Education Commission (KM201811417002,KM201911417001, KM201711417005), Big Data Collaborative Innovation Center for Intelligent Driving(CYXC1902),and Beijing Natural Science Foundation(4184088)

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Abstract: Face detection is finding and locating all faces from the input pictures or videos.In order to solve the difficulties caused by the diversity of face size,especially small-sized faces,a new single shot small-scale face detection method is presented based on feature map fusion.The method first selects the feature map to be detected reasonably,and uses different feature maps to detect faces of different sizes.Then,by combining the deep feature map and the shallow feature map,the context information is introduced reasonably,thereby improving the detection precision of the small-sized face.The proposed model is trained and tested on the NVIDIA GTX TATAN X using the WIDERFACE dataset.The results on the three test subsets of WIDERFACE are 88.9% (hard),93.5% (medium),94.3% (easy) AP,at 39 fps.It is superior to other excellent detection methods in both detection accuracy and detection speed.

Key words: Contextual information, Feature map fusion, Single shot, Small size

CLC Number: 

  • TP391
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