Semi-Dense U-Net: A Novel U-Net Architecture for Face Detection

被引:0
作者
Pai, Ganesh [1 ]
Kumari, M. Sharmila [2 ]
机构
[1] Nitte, NMAM Inst Technol, Dept Comp Sci & Engn, Karnataka 574110, India
[2] VTU, PA Coll Engn, Dept Comp Sci & Engn, Mangalore 574153, Karnataka, India
关键词
Semi-Dense U-Net; face detection; segmentation; U-Net;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Face detection and localization has been a major field of study in facial analysis and computer vision. Several convolutional neural network-based architectures have been proposed in the literature such as cascaded approach, single -stage and two-stage architectures. Using image segmentation based technique for object/face detection and recognition have been an alternative approach recently being employed. In this paper, we propose detection of faces by using U-net segmentation architectures. Motivated from DenseNet, a variant of U-net, called Semi-Dense U-Net, is designed in order to improve the binary masks generated by the segmentation model and further post-processed to detect faces. The proposed U-Net model have been trained and tested on FDDB, Wider face and Open Image dataset and compared with state-of-the-art algorithms. We could successfully achieve dice coefficient of 95.68% and average precision of 91.60% on a set of test data from OpenImage dataset.
引用
收藏
页码:406 / 414
页数:9
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