Autonomous Machine Learning Framework for Detecting People Aliveness

Song, M.H.; Dong Kim, S.

doi:10.1109/SAS.2019.8706058

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Autonomous Machine Learning Framework for Detecting People Aliveness

Song, M.H.;
Dong Kim, S.

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초록

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.

키워드

Accuracy; Detection Framework; Machine Learning; People Aliveness; Computer programming; Learning systems; Optimization; Accuracy; Autonomous computing; Continuous optimization; Conventional approach; Design and implementations; Detection framework; Machine learning models; People Aliveness; Machine learning

제목: Autonomous Machine Learning Framework for Detecting People Aliveness

저자: Song, M.H.; Dong Kim, S.

DOI: 10.1109/SAS.2019.8706058

발행일: 2019-05

유형: Conference Paper

저널명: SAS 2019 - 2019 IEEE Sensors Applications Symposium, Conference Proceedings

페이지: 870658