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Combining Stacked Denoising Autoencoders and Random Forests for Face Detection / Michael Edwards, Jingjing Deng, Xianghua Xie

Advanced Concepts for Intelligent Vision Systems, Volume: 10016, Pages: 349 - 360

Swansea University Authors: Michael Edwards, Jingjing Deng, Xianghua Xie

DOI (Published version): 10.1007/978-3-319-48680-2_31

Abstract

In this work, we propose a novel method that uses stacked denoising autoencoders (SdA) for feature extraction and random forests (RF) for object-background classification in a classical cascading framework. This architecture allows much simpler neural network structures, resulting in efficient train...

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Published in: Advanced Concepts for Intelligent Vision Systems
ISBN: 978-3-319-48679-6 978-3-319-48680-2
Published: 2016
URI: https://cronfa.swan.ac.uk/Record/cronfa32104
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spelling 2017-04-26T17:40:42.2110648 v2 32104 2017-02-24 Combining Stacked Denoising Autoencoders and Random Forests for Face Detection 684864a1ce01c3d774e83ed55e41770e 0000-0003-3367-969X Michael Edwards Michael Edwards true false 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 In this work, we propose a novel method that uses stacked denoising autoencoders (SdA) for feature extraction and random forests (RF) for object-background classification in a classical cascading framework. This architecture allows much simpler neural network structures, resulting in efficient training and detection. The proposed face detector was evaluated on two publicly available datasets and produced promising results. Book chapter Advanced Concepts for Intelligent Vision Systems 10016 349 360 978-3-319-48679-6 978-3-319-48680-2 Deep Learning, Neural Network, Random Forests, Autoencoder, Face Detection, Machine Learning, Computer Vision 31 10 2016 2016-10-31 10.1007/978-3-319-48680-2_31 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2017-04-26T17:40:42.2110648 2017-02-24T23:36:10.0833448 College of Science Computer Science Michael Edwards 0000-0003-3367-969X 1 Jingjing Deng 0000-0001-9274-651X 2 Xianghua Xie 0000-0002-2701-8660 3 Michael Edwards 4 0032104-21032017091411.pdf acivs16jd.pdf 2017-03-21T09:14:11.4530000 Output 8853997 application/pdf Accepted Manuscript true 2016-10-01T00:00:00.0000000 true eng
title Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
spellingShingle Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
Michael, Edwards
Jingjing, Deng
Xianghua, Xie
title_short Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
title_full Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
title_fullStr Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
title_full_unstemmed Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
title_sort Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
author_id_str_mv 684864a1ce01c3d774e83ed55e41770e
6f6d01d585363d6dc1622640bb4fcb3f
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 684864a1ce01c3d774e83ed55e41770e_***_Michael, Edwards
6f6d01d585363d6dc1622640bb4fcb3f_***_Jingjing, Deng
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua, Xie
author Michael, Edwards
Jingjing, Deng
Xianghua, Xie
author2 Michael Edwards
Jingjing Deng
Xianghua Xie
Michael Edwards
format Book chapter
container_title Advanced Concepts for Intelligent Vision Systems
container_volume 10016
container_start_page 349
publishDate 2016
institution Swansea University
isbn 978-3-319-48679-6
978-3-319-48680-2
doi_str_mv 10.1007/978-3-319-48680-2_31
college_str College of Science
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hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
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description In this work, we propose a novel method that uses stacked denoising autoencoders (SdA) for feature extraction and random forests (RF) for object-background classification in a classical cascading framework. This architecture allows much simpler neural network structures, resulting in efficient training and detection. The proposed face detector was evaluated on two publicly available datasets and produced promising results.
published_date 2016-10-31T03:47:23Z
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score 10.843154