Title
Dsfd: Dual Shot Face Detector
Abstract
In this paper,we propose a novel face detection network with three novel contributions that address three key aspects of face detection, including better feature learning,progressive loss design and anchor assign based data augmentation, respectively. First, we propose a Feature Enhance Module (FEM) for enhancing the original feature maps to extend the single shot detector to dual shot detector Second, we adopt Progressive Anchor Loss (PAL) computed by two different sets of anchors to effectively facilitate the features. Third, we use an Improved Anchor Matching (IAM) by integrating novel anchor assign strategy into data augmentation to provide better initialization for the regressor Since these techniques are all related to the two-stream design, we name the proposed network as Dual Shot Face Detector (DSFD). Extensive experiments on popular benchmarks, WIDER FACE and FDDB, demonstrate the superiority of DSFD over the state-of-the-art face detectors.
Year
DOI
Venue
2018
10.1109/CVPR.2019.00520
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Pattern recognition,Computer science,Finite element method,Artificial intelligence,Initialization,Face detection,Detector,Feature learning
Journal
abs/1810.10220
ISSN
Citations 
PageRank 
1063-6919
14
0.54
References 
Authors
20
9
Name
Order
Citations
PageRank
Jian Li1141.21
Yabiao Wang2217.05
Changan Wang3140.54
Ying Tai421325.74
Jianjun Qian538227.74
Jian Yang66102339.77
Chengjie Wang74319.03
Jilin Li8488.94
Feiyue Huang922641.86