Face detection is the task of detecting faces in a photo or video (and distinguishing them from other objects).
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We present BlazeFace, a lightweight and well-performing face detector tailored for mobile GPU inference.
In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD), less than 0. 1 million, as well as achieving comparable performance to deep heavy detectors.
#8 best model for Face Detection on WIDER Face (Hard)
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
In particular, we adopt a region-based object detection structure with two carefully designed detectors to separately pay attention to the human body and body parts in a coarse-to-fine manner, which we call Detector-in-Detector network (DID-Net).
In this paper, we propose that the robustness of a face detector against hard faces can be improved by learning small faces on hard images.
#2 best model for Face Detection on PASCAL Face
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
In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module.