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Conference Paper/Proceeding/Abstract 632 views 99 downloads

Nested Shallow CNN-Cascade for Face Detection in the Wild / Jingjing Deng; Xianghua Xie

2017 IEEE 12th International Conference on Automatic Face & Gesture Recognition, Pages: 165 - 172

Swansea University Authors: Jingjing, Deng, Xianghua, Xie

DOI (Published version): 10.1109/FG.2017.29

Abstract

We propose a nested CNN-cascade learning algorithm that adopts shallow neural network architectures that allow efficient and progressive elimination of negative hypothesis from easy to hard via self-learning discriminative representations from coarse to fine scales. The face detection problem is con...

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Published in: 2017 IEEE 12th International Conference on Automatic Face & Gesture Recognition
ISBN: 978-1-5090-4024-7 978-1-5090-4023-0
Published: IEEE 2017
Online Access: http://csvision.swan.ac.uk/uploads/Site/Publication/jd17fg.pdf
URI: https://cronfa.swan.ac.uk/Record/cronfa32108
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spelling 2020-07-31T09:40:15.0286012 v2 32108 2017-02-24 Nested Shallow CNN-Cascade for Face Detection in the Wild 6f6d01d585363d6dc1622640bb4fcb3f 0000-0001-9274-651X Jingjing Deng Jingjing Deng true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2017-02-24 SCS We propose a nested CNN-cascade learning algorithm that adopts shallow neural network architectures that allow efficient and progressive elimination of negative hypothesis from easy to hard via self-learning discriminative representations from coarse to fine scales. The face detection problem is considered as solving three sub-problems: eliminating easy background with a simple but fast model, then localising the face region with a soft-cascade, followed by precise detection and localisation by verifying retained regions with a deeper and stronger model. Conference Paper/Proceeding/Abstract 2017 IEEE 12th International Conference on Automatic Face & Gesture Recognition 165 172 IEEE 978-1-5090-4024-7 978-1-5090-4023-0 Deep Learning, Neural Network, Face Detection, CNN 29 6 2017 2017-06-29 10.1109/FG.2017.29 http://csvision.swan.ac.uk/uploads/Site/Publication/jd17fg.pdf COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-07-31T09:40:15.0286012 2017-02-24T23:46:07.2461762 College of Science Computer Science Jingjing Deng 0000-0001-9274-651X 1 Xianghua Xie 0000-0002-2701-8660 2 0032108-27052017151854.pdf FG2017CRC.pdf 2017-05-27T15:18:54.0230000 Output 17090590 application/pdf Accepted Manuscript true 2017-05-27T00:00:00.0000000 true eng
title Nested Shallow CNN-Cascade for Face Detection in the Wild
spellingShingle Nested Shallow CNN-Cascade for Face Detection in the Wild
Jingjing, Deng
Xianghua, Xie
title_short Nested Shallow CNN-Cascade for Face Detection in the Wild
title_full Nested Shallow CNN-Cascade for Face Detection in the Wild
title_fullStr Nested Shallow CNN-Cascade for Face Detection in the Wild
title_full_unstemmed Nested Shallow CNN-Cascade for Face Detection in the Wild
title_sort Nested Shallow CNN-Cascade for Face Detection in the Wild
author_id_str_mv 6f6d01d585363d6dc1622640bb4fcb3f
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 6f6d01d585363d6dc1622640bb4fcb3f_***_Jingjing, Deng
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua, Xie
author Jingjing, Deng
Xianghua, Xie
author2 Jingjing Deng
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title 2017 IEEE 12th International Conference on Automatic Face & Gesture Recognition
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institution Swansea University
isbn 978-1-5090-4024-7
978-1-5090-4023-0
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publisher IEEE
college_str College of Science
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hierarchy_top_title College of Science
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url http://csvision.swan.ac.uk/uploads/Site/Publication/jd17fg.pdf
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description We propose a nested CNN-cascade learning algorithm that adopts shallow neural network architectures that allow efficient and progressive elimination of negative hypothesis from easy to hard via self-learning discriminative representations from coarse to fine scales. The face detection problem is considered as solving three sub-problems: eliminating easy background with a simple but fast model, then localising the face region with a soft-cascade, followed by precise detection and localisation by verifying retained regions with a deeper and stronger model.
published_date 2017-06-29T03:49:14Z
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score 10.751705