Book chapter 1488 views 295 downloads
Combining Stacked Denoising Autoencoders and Random Forests for Face Detection
Mike Edwards 
,
Jingjing Deng,
Xianghua Xie ,
Michael Edwards
,
Jingjing Deng,
Xianghua Xie ,
Michael Edwards
Advanced Concepts for Intelligent Vision Systems, Volume: 10016, Pages: 349 - 360
Swansea University Authors:
Mike Edwards , Jingjing Deng, Xianghua Xie
-
PDF | Accepted Manuscript
Download (8.48MB)
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...
| 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 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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 training and detection. The proposed face detector was evaluated on two publicly available datasets and produced promising results. |
|---|---|
| Keywords: |
Deep Learning, Neural Network, Random Forests, Autoencoder, Face Detection, Machine Learning, Computer Vision |
| College: |
Faculty of Science and Engineering |
| Start Page: |
349 |
| End Page: |
360 |