Face detection is the task of detecting faces in a photo or video (and distinguishing them from other objects).
( Image credit: FaceBoxes )
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Face detection and alignment in unconstrained environment is always deployed on edge devices which have limited memory storage and low computing power.
To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet.
SOTA for Face Recognition on AgeDB-30
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