Robust person recognition using CNN
© 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 ener...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/69901 |
| _version_ | 1848762162284068864 |
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| author | Chen, M. Lin, Q. Allebach, J. Zhu, Maggie |
| author_facet | Chen, M. Lin, Q. Allebach, J. Zhu, Maggie |
| author_sort | Chen, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:43:11Z |
| format | Conference Paper |
| id | curtin-20.500.11937-69901 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:43:11Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-699012018-08-08T04:56:27Z Robust person recognition using CNN Chen, M. Lin, Q. Allebach, J. Zhu, Maggie © 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. 2017 Conference Paper http://hdl.handle.net/20.500.11937/69901 10.2352/ISSN.2470-1173.2017.10.IMAWM-165 restricted |
| spellingShingle | Chen, M. Lin, Q. Allebach, J. Zhu, Maggie Robust person recognition using CNN |
| title | Robust person recognition using CNN |
| title_full | Robust person recognition using CNN |
| title_fullStr | Robust person recognition using CNN |
| title_full_unstemmed | Robust person recognition using CNN |
| title_short | Robust person recognition using CNN |
| title_sort | robust person recognition using cnn |
| url | http://hdl.handle.net/20.500.11937/69901 |