中国科学院重庆绿色智能技术研究院机构知识库

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	Hierarchical bilinear network for high performance face detection
	Lv, Jiangjing1,2; Shao, Xiaohu1,2; Xing, Junliang3; Liu, Pengcheng1; Zhou, Xiangdong1; Zheu, Xi1
	2018
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.
语种	英语
DOI	10.1109/ICIP.2017.8296314
会议(录)名称	24th IEEE International Conference on Image Processing, ICIP 2017
页码	415-419
收录类别	EI
会议地点	Beijing, China
会议日期	September 17, 2017 - September 20, 2017