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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search

Xue, Dongfei, Wang, Xiaonian, Zhu, Jin, Davis, Darryl, Wang, Bing, Zhao, Wenbing, Peng, Yonghong and Cheng, Yongqiang (2018) An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search. Applied System Innovation, 1 (3). p. 33. ISSN 2571-5577

Item Type: Article


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.

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Depositing User: Yonghong Peng


Item ID: 10177
Identification Number:
ISSN: 2571-5577
Official URL:

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Catalogue record

Date Deposited: 20 Nov 2018 14:53
Last Modified: 18 Dec 2019 16:07


Author: Dongfei Xue
Author: Xiaonian Wang
Author: Jin Zhu
Author: Darryl Davis
Author: Bing Wang
Author: Wenbing Zhao
Author: Yonghong Peng
Author: Yongqiang Cheng

University Divisions

Faculty of Technology
Faculty of Technology > FOT Executive


Computing > Data Science
Computing > Artificial Intelligence

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