Face detection is the task of detecting faces in a photo or video (and distinguishing them from other objects).
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With the rapid development of deep convolutional neural network, face detection has made great progress in recent years.
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 WIDER Face (Easy)
In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module.
Rotation-invariant face detection, i. e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances.
We show that the Inception+SVM model establishes a state-of-the-art F1 score on the task of gender recognition of cartoon faces.