RefineFace: Refinement Neural Network for High Performance Face Detection
Shifeng Zhang, Cheng Chi, Zhen Lei, Stan Z. Li

TL;DR
RefineFace is a single-shot face detector that integrates multiple modules to improve accuracy and efficiency, especially for tiny faces, achieving state-of-the-art results on standard benchmarks.
Contribution
The paper introduces a novel refinement neural network with five modules designed to enhance high-performance face detection, particularly for tiny and challenging faces.
Findings
Achieves state-of-the-art results on WIDER FACE and other benchmarks.
Runs at 37.3 FPS with ResNet-18 on VGA images.
Effectively detects faces in extreme poses and scales.
Abstract
Face detection has achieved significant progress in recent years. However, high performance face detection still remains a very challenging problem, especially when there exists many tiny faces. In this paper, we present a single-shot refinement face detector namely RefineFace to achieve high performance. Specifically, it consists of five modules: Selective Two-step Regression (STR), Selective Two-step Classification (STC), Scale-aware Margin Loss (SML), Feature Supervision Module (FSM) and Receptive Field Enhancement (RFE). To enhance the regression ability for high location accuracy, STR coarsely adjusts locations and sizes of anchors from high level detection layers to provide better initialization for subsequent regressor. To improve the classification ability for high recall efficiency, STC first filters out most simple negatives from low level detection layers to reduce search…
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Taxonomy
TopicsFace recognition and analysis · Face and Expression Recognition · Video Surveillance and Tracking Methods
