Autonomous Machine Learning Framework for Detecting People Aliveness
- Authors
- Song, M.H.; Dong Kim, S.
- Issue Date
- May-2019
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Accuracy; Detection Framework; Machine Learning; People Aliveness
- Citation
- SAS 2019 - 2019 IEEE Sensors Applications Symposium, Conference Proceedings, pp.870658
- Journal Title
- SAS 2019 - 2019 IEEE Sensors Applications Symposium, Conference Proceedings
- Start Page
- 870658
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/34771
- DOI
- 10.1109/SAS.2019.8706058
- ISSN
- 0000-0000
- Abstract
- Determining the aliveness of people is essential for various people safety systems. While the accuracy of the aliveness determination is a key concern, there exist technical challenges in the aliveness determination with high accuracy. Our approach to the challenges is to incorporate a set of effective design tactics into a software framework. We employ machine learning models in the detection process and apply the continuous optimization of the aliveness model using autonomous computing principles. This paper presents a comprehensive framework which consists of design and implementation. Our extensive experiments show that the accuracy of aliveness determination with this framework outperforms at least 30% of conventional approaches. © 2019 IEEE.
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