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An adaptive ensemble approach to ambient intelligence assisted people search

Xue, Dongfei; Wang, Xiaonian; Zhu, Jin; Davis, Darryl N.; Wang, Bing; Zhao, Wenbing; Peng, Yonghong; Cheng, Yongqiang

Authors

Dongfei Xue

Xiaonian Wang

Jin Zhu

Darryl N. Davis

Wenbing Zhao

Yonghong Peng



Abstract

Some machine learning algorithms have shown a better overall recognition rate for facial recognition than humans, provided that the models are trained with massive image databases of human faces. However, it is still a challenge to use existing algorithms to perform localized people search tasks where the recognition must be done in real time, and where only a small face database is accessible. A localized people search is essential to enable robot–human interactions. In this article, we propose a novel adaptive ensemble approach to improve facial recognition rates while maintaining low computational costs, by combining lightweight local binary classifiers with global pre-trained binary classifiers. In this approach, the robot is placed in an ambient intelligence environment that makes it aware of local context changes. Our method addresses the extreme unbalance of false positive results when it is used in local dataset classifications. Furthermore, it reduces the errors caused by affine deformation in face frontalization, and by poor camera focus. Our approach shows a higher recognition rate compared to a pre-trained global classifier using a benchmark database under various resolution images, and demonstrates good efficacy in real-time tasks.

Citation

Xue, D., Wang, X., Zhu, J., Davis, D. N., Wang, B., Zhao, W., …Cheng, Y. (2018). An adaptive ensemble approach to ambient intelligence assisted people search. Applied System Innovation, 1(3), 1-18. https://doi.org/10.3390/asi1030033

Journal Article Type Article
Acceptance Date Aug 28, 2018
Online Publication Date Sep 3, 2018
Publication Date Sep 1, 2018
Deposit Date Aug 28, 2018
Publicly Available Date Sep 6, 2018
Journal Applied System Innovation
Electronic ISSN 2571-5577
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 1
Issue 3
Article Number 33
Pages 1-18
Series ISSN 2571-5577
DOI https://doi.org/10.3390/asi1030033
Keywords Machine learning; Mobile robots; Robot vision; Navigation; Classifier ensemble; People
Public URL https://hull-repository.worktribe.com/output/1001765
Publisher URL http://www.mdpi.com/2571-5577/1/3/33
Copyright Statement This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Copyright Statement
©2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).





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