Hierarchical bilinear network for high performance face detection
Lv, Jiangjing1,2; Shao, Xiaohu1,2; Xing, Junliang3; Liu, Pengcheng1; Zhou, Xiangdong1; Zheu, Xi1
2018
会议日期September 17, 2017 - September 20, 2017
会议地点Beijing, China
DOI10.1109/ICIP.2017.8296314
页码415-419
英文摘要Deep Convolutional Networks (DCNs) have achieved great success in face detection. Most architectures of the DCN-based methods, however, suffer from multiple separated steps and large-size models, which increase the training complexity and also slow down the testing speed. In this paper, we propose an efficient end-to-end architecture, called Hierarchical Bilinear Network (HBN), for fast and accurate face detection. It mainly consists of two parts: the Backbone Network and the Bilinear Network. The Backbone Network generates hierarchical feature maps for efficiently characterizing faces of different scales, while the Bilinear Network classifies the regions and regresses the face bounding-boxes on each feature map by introducing the Inception module and weights sharing. Benefited from the characters of the proposed architecture, it obtains a better comprehensive performance regarding the model effectiveness, running efficiency, and parameter size, compared with other DCN-based methods. Extensive experimental results demonstrate that our detector achieves competitive accuracy on both the FDDB database and the WIDER FACE database, while still runs in real time (about 69 FPS on a Titan Black GPU) with a tiny size (2.2 MB) model. © 2017 IEEE.
会议录24th IEEE International Conference on Image Processing, ICIP 2017
语种英语
ISSN号15224880
内容类型会议论文
源URL[http://119.78.100.138/handle/2HOD01W0/7968]  
专题中国科学院重庆绿色智能技术研究院
作者单位1.Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing; 400714, China;
2.China University of Chinese Academy of Sciences, Beijing; 100049, China;
3.National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing; 100190, China
推荐引用方式
GB/T 7714
Lv, Jiangjing,Shao, Xiaohu,Xing, Junliang,et al. Hierarchical bilinear network for high performance face detection[C]. 见:. Beijing, China. September 17, 2017 - September 20, 2017.
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