Paper Info
Title
SSFD: A Face Detector using A Single-scale Feature Map
Abstract
In this paper, we present a simple but effective face detector (dubbed SSFD), which can localize multi-scale faces. Unlike other multi-scale feature detectors which learn multi-scale features or feature pyramids aggregated from different scale feature maps, SSFD only depends on a single-scale input image and a single-scale feature map to detect faces of various scales. In SSFD, transposed convolutions are leveraged to increase the resolution of feature maps with different strides, which can maintain adequate information for occluded and small faces. In addition, dilated convolutions are deployed to increase the receptive field size, which contributes to obtaining discriminative contextual information. SSFD, which is based on the VGG-16 network, outperforms the ResNet101-based Scale-Face as well as the VGG16-based HR on the WIDER FACE validation dataset.
Year
DOI
Venue
2018
10.1109/BTAS.2018.8698570
2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)
Field
DocType
ISSN
Receptive field,Contextual information,Feature detection,Pattern recognition,Convolution,Computer science,Artificial intelligence,Detector,Discriminative model
Conference
2474-9680
ISBN
Citations 
PageRank 
978-1-5386-7180-1
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Lei Shi130655.98
Xiang Xu2305.58
Ioannis A. Kakadiaris31910203.66