Face Detection:A Deep Convolutional Network Method Based on Grouped Facial Part
In this paper,a novel method is proposed for face detection,which is of simple structure but robust to severe occlusion.In detail,the size-free images are firstly segmented to a series of candidate windows.Then these candidate windows are further filtered by grouped facial part networks to generate a set of windows which may contain faces.Finally,the set of face proposals are input to a multi-task deep convolutional network(DCN)for further classification and calibration.Importantly,we take the spatial position relations of local facial parts into consideration and find it helpful to handle the severe occlusion.Our method achieves outstanding performance on the widely used datasets FDDB and AFW,compared to the other proposed face detectors.Especially on FDDB,our method achieves a high recall rate of 90.13%.
face detection grouped facial part deep learning deep convolutional networks
Xianbo Yu Yuzhuo Fu Ting Liu
School of Microelectronics,Shanghai Jiao Tong University Shanghai,China,200240
国际会议
重庆
英文
515-519
2017-03-25（万方平台首次上网日期，不代表论文的发表时间）