Face Attention Network: An Effective Face Detector for the Occluded Faces (2017-11-20T00:00:00.000000Z)

TL;DR

A novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed, and is integrated with the anchor assign strategy and data augmentation techniques.

Abstract

The performance of face detection has been largely improved with the development of convolutional neural network. However, the occlusion issue due to mask and sunglasses, is still a challenging problem. The improvement on the recall of these occluded cases usually brings the risk of high false positives. In this paper, we present a novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed. More specifically, we propose a new anchor-level attention, which will highlight the features from the face region. Integrated with our anchor assign strategy and data augmentation techniques, we obtain state-of-art results on public face detection benchmarks like WiderFace and MAFA. The code will be released for reproduction.

Authors

Gang Yu

10 papers

Ye Yuan

2 papers

Jianfeng Wang

4 papers

TL;DR

Abstract

Authors

References41 items

Adversarial Occlusion-aware Face Detection

Masquer Hunter: Adversarial Occlusion-aware Face Detection

S^3FD: Single Shot Scale-Invariant Face Detector

SSH: Single Stage Headless Face Detector

Focal Loss for Dense Object Detection

Recurrent Scale Approximation for Object Detection in CNN

Detecting Masked Faces in the Wild with LLE-CNNs

Scale-Aware Face Detection

Face Detection through Scale-Friendly Deep Convolutional Networks

Finding Tiny Faces

Feature Pyramid Networks for Object Detection

To boost or not to boost? On the limits of boosted trees for object detection

Grid Loss: Detecting Occluded Faces

UnitBox: An Advanced Object Detection Network

Supervised Transformer Network for Efficient Face Detection

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

Face Detection with the Faster R-CNN

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

Training Region-Based Object Detectors with Online Hard Example Mining

Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Deep Residual Learning for Image Recognition

SSD: Single Shot MultiBox Detector

WIDER FACE: A Face Detection Benchmark

From Facial Parts Responses to Face Detection: A Deep Learning Approach

DenseBox: Unifying Landmark Localization with End to End Object Detection

You Only Look Once: Unified, Real-Time Object Detection

A convolutional neural network cascade for face detection

High-fidelity Pose and Expression Normalization for face recognition in the wild

Spatial Transformer Networks

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Fast R-CNN

FaceNet: A unified embedding for face recognition and clustering

Fast Feature Pyramids for Object Detection

Aggregate channel features for multi-view face detection

Microsoft COCO: Common Objects in Context

Supervised Descent Method and Its Applications to Face Alignment

A discriminatively trained, multiscale, deformable part model

Histograms of oriented gradients for human detection

Robust Real-Time Face Detection

Face alignment across large poses: A 3d solution

Deep Face Recognition

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