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Combining Stacked Denoising Autoencoders and Random Forests for Face Detection / Jingjing, Deng; Xianghua, Xie
Advanced Concepts for Intelligent Vision Systems, Volume: 10016, Pages: 349 - 360
Swansesa University Authors: Jingjing, Deng, Xianghua, Xie
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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 |
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| ISBN: | 978-3-319-48679-6 978-3-319-48680-2 |
| Published: |
2016
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa32104 |
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| 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. |
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| Keywords: |
Deep Learning, Neural Network, Random Forests, Autoencoder, Face Detection, Machine Learning, Computer Vision |
| College: |
College of Science |
| Start Page: |
349 |
| End Page: |
360 |