Open AccessA new face detection method based on Faster RCNN
Author(s) -
Yan He,
Xiaotang Wang,
Yuhan Liu,
Yuning Zhang,
Huan Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012209
Subject(s) - face detection , artificial intelligence , computer science , weighting , robustness (evolution) , face (sociological concept) , pattern recognition (psychology) , computer vision , frame (networking) , facial recognition system , object class detection , physics , telecommunications , social science , biochemistry , chemistry , sociology , acoustics , gene
The Non-Maximum Suppression (NMS) method based on Faster RCNN uses hard threshold to identify candidate face frames which to be detected. In complex scenes with partial occlusion of the face and uneven lighting, the phenomenon of missing and false detection of the face is prone to occur. Aiming at this problem, this paper proposes a new face detection method to extract face features through CNN, and generates a large number of face candidate frames which to be detected by the Region Proposal Network (RPN); the hard threshold of NMS is improved by linear weighting method, and the face candidate frame is screened by the linearly weighted NMS. Comparison experiment results show that on the FDDB dataset, the new face detection method in this paper can effectively avoid the false detection and missed detection of multiple faces under partial occlusion and uneven illumination, also has high detection accuracy and robustness.