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Open AccessArticle

An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search

1
School of Engineering and Computer Science, University of Hull, Hull HU6 7RX, UK
2
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China
3
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44011, USA
4
Faculty of Computer Science, University of Sunderland, St Peters Campus, Sunderland SR6 0DD, UK
*
Author to whom correspondence should be addressed.
Appl. Syst. Innov. 2018, 1(3), 33; https://doi.org/10.3390/asi1030033
Received: 13 July 2018 / Revised: 23 August 2018 / Accepted: 28 August 2018 / Published: 3 September 2018
(This article belongs to the Special Issue Healthcare System Innovation)
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. View Full-Text
Keywords: machine learning; mobile robots; robot vision; navigation; classifier ensemble; people search; ambient intelligence machine learning; mobile robots; robot vision; navigation; classifier ensemble; people search; ambient intelligence
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Xue, D.; Wang, X.; Zhu, J.; Davis, D.N.; Wang, B.; Zhao, W.; Peng, Y.; Cheng, Y. An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search. Appl. Syst. Innov. 2018, 1, 33.

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