Learn to Recognise : Exploring Priors of Sparse Face Recognition on Smartphones
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22062508
- Type
- D - Journal article
- DOI
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10.1109/TMC.2016.2593919
- Title of journal
- IEEE Transactions on Mobile Computing
- Article number
- -
- First page
- 1705
- Volume
- 16
- Issue
- 6
- ISSN
- 1536-1233
- Open access status
- Technical exception
- Month of publication
- August
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed application of face recognition on resource-constrained mobile devices led to the funding “Study on the Cyber Security of Wearable IoTs Based on Gait Analysis” via the National Natural Science Foundation of China (link in Chinese: https://www.sciping.com/22907.html). This work led to further collaboration between UNSW and WBS Technology company. Furthermore, the application of face recognition on resource-constrained devices led to the success of the funding: ARC (Australia Research Council) linkage project grant 2016R1 on “Lightweight security framework for Low-Power Wide-Area Network (LPWAN).” (Australian $468,000, Leading Investigator: Dr Wen Hu, Contact: wen.hu@unsw.edu.au ).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -