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

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

Swansea University Author: Xie, Xianghua

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 2017-04-26T17:40:42Z v2 32104 2017-02-24 Combining Stacked Denoising Autoencoders and Random Forests for Face Detection Xianghua Xie Xianghua Xie true 0000-0002-2701-8660 false b334d40963c7a2f435f06d2c26c74e11 53b7e8cec1e3c035df428f36f80bdea5 ulOdsUw0nzyNlMFzZoDyVp320YwKTXZRCaAvm14NMEw= 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. Chapter in book 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 0 10 2016 2016-10-01 10.1007/978-3-319-48680-2_31 College of Science Computer Science CSCI SCS Visual Computing None 2017-04-26T17:40:42Z 2017-02-24T23:36:10Z College of Science Computer Science Jingjing Deng 1 Xianghua Xie 2 Michael Edwards 3 0032104-21032017091411.pdf acivs16jd.pdf 2017-03-21T09:14:11Z Output 8853997 application/pdf AM true Updated Copyright 21/03/2017 2016-10-01T00:00:00 true eng
title Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
spellingShingle Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
Xie, Xianghua
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 b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xie, Xianghua
author Xie, Xianghua
author2 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
hierarchytype
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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
document_store_str 1
active_str 1
researchgroup_str Visual Computing
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 2016-10-01T12:39:46Z
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score 10.801898