Face detection is the task of detecting faces in a photo or video (and distinguishing them from other objects).
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We describe a method for visual object detection based on an ensemble of optimized decision trees organized in a cascade of rejectors.
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.
SOTA for Face Detection on FDDB
Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions.
#13 best model for Face Detection on WIDER Face (Easy)
Under the new schema, the proposed method can achieve superior accuracy (WIDER FACE Val/Test -- Easy: 0. 910/0. 896, Medium: 0. 881/0. 865, Hard: 0. 780/0. 770; FDDB -- discontinuous: 0. 973, continuous: 0. 724).
#6 best model for Face Detection on FDDB
Face detection and alignment in unconstrained environment is always deployed on edge devices which have limited memory storage and low computing power.
The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales.
#4 best model for Face Detection on PASCAL Face
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.
#2 best model for Face Detection on Annotated Faces in the Wild
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection.
#11 best model for Face Detection on WIDER Face (Hard)