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dc.contributor.authorChen, M.
dc.contributor.authorLin, Q.
dc.contributor.authorAllebach, J.
dc.contributor.authorZhu, Maggie
dc.date.accessioned2018-08-08T04:42:44Z
dc.date.available2018-08-08T04:42:44Z
dc.date.created2018-08-08T03:50:50Z
dc.date.issued2017
dc.identifier.citationChen, M. and Lin, Q. and Allebach, J. and Zhu, M. 2017. Robust person recognition using CNN, pp. 45-50.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/69901
dc.identifier.doi10.2352/ISSN.2470-1173.2017.10.IMAWM-165
dc.description.abstract

© 2017, Society for Imaging Science and Technology. Person detection and recognition has many applications in autonomous driving, smart home and smart office applications. Knowledge about the presence of a person in the environment can be used in safety solutions such as collision avoidance, in energy conservation solutions such as turning lights and air-conditioning off when there is no person around, and in meeting and collaboration solutions such as locating a vacant room. In this paper, we present a solution that can reliably detect and recognize persons under different lighting conditions and pose based on head detection and recognition using deep learning. The system is proved to achieve good results on a challenging dataset.

dc.titleRobust person recognition using CNN
dc.typeConference Paper
dcterms.source.startPage45
dcterms.source.endPage50
dcterms.source.issn2470-1173
dcterms.source.titleIS and T International Symposium on Electronic Imaging Science and Technology
dcterms.source.seriesIS and T International Symposium on Electronic Imaging Science and Technology
curtin.departmentSchool of Public Health
curtin.accessStatusFulltext not available


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