Face detection using deep learning: An improved faster RCNN approach

In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, inclu...

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Main Authors: SUN, Xudong, WU, Pengcheng, HOI, Steven C. H.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/3998
https://ink.library.smu.edu.sg/context/sis_research/article/5000/viewcontent/Face_detection_using_deep_learning__An_improved_faster_RCNN_approach.pdf
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spelling sg-smu-ink.sis_research-50002018-12-07T09:15:02Z Face detection using deep learning: An improved faster RCNN approach SUN, Xudong WU, Pengcheng HOI, Steven C. H. In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark 2018-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3998 info:doi/10.1016/j.neucom.2018.03.030 https://ink.library.smu.edu.sg/context/sis_research/article/5000/viewcontent/Face_detection_using_deep_learning__An_improved_faster_RCNN_approach.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Face detection Faster RCNN Convolutional neural networks (CNN) Feature concatenation Hard negative miningMulti-scale training Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Face detection
Faster RCNN
Convolutional neural networks (CNN)
Feature concatenation
Hard negative miningMulti-scale training
Databases and Information Systems
spellingShingle Face detection
Faster RCNN
Convolutional neural networks (CNN)
Feature concatenation
Hard negative miningMulti-scale training
Databases and Information Systems
SUN, Xudong
WU, Pengcheng
HOI, Steven C. H.
Face detection using deep learning: An improved faster RCNN approach
description In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark
format text
author SUN, Xudong
WU, Pengcheng
HOI, Steven C. H.
author_facet SUN, Xudong
WU, Pengcheng
HOI, Steven C. H.
author_sort SUN, Xudong
title Face detection using deep learning: An improved faster RCNN approach
title_short Face detection using deep learning: An improved faster RCNN approach
title_full Face detection using deep learning: An improved faster RCNN approach
title_fullStr Face detection using deep learning: An improved faster RCNN approach
title_full_unstemmed Face detection using deep learning: An improved faster RCNN approach
title_sort face detection using deep learning: an improved faster rcnn approach
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/3998
https://ink.library.smu.edu.sg/context/sis_research/article/5000/viewcontent/Face_detection_using_deep_learning__An_improved_faster_RCNN_approach.pdf
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