Imperial College London

DrBennyLo

Faculty of MedicineDepartment of Surgery & Cancer

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

B414BBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Sun:2019:10.1109/BSN.2019.8771075,
author = {Sun, Y and Lo, FP-W and Lo, B},
doi = {10.1109/BSN.2019.8771075},
publisher = {IEEE},
title = {A deep learning approach on gender and age recognition using a single inertial sensor},
url = {http://dx.doi.org/10.1109/BSN.2019.8771075},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Extracting human attributes, such as gender and age, from biometrics have received much attention in recent years. Gender and age recognition can provide crucial information for applications such as security, healthcare, and gaming. In this paper, a novel deep learning approach on gender and age recognition using a single inertial sensors is proposed. The proposed approach is tested using the largest available inertial sensor-based gait database with data collected from more than 700 subjects. To demonstrate the robustness and effectiveness of the proposed approach, 10 trials of inter-subject Monte-Carlo cross validation were conducted, and the results show that the proposed approach can achieve an averaged accuracy of 86.6%±2.4% for distinguishing two age groups: teen and adult, and recognizing gender with averaged accuracies of 88.6%±2.5% and 73.9%±2.8% for adults and teens respectively.
AU - Sun,Y
AU - Lo,FP-W
AU - Lo,B
DO - 10.1109/BSN.2019.8771075
PB - IEEE
PY - 2019///
SN - 2376-8886
TI - A deep learning approach on gender and age recognition using a single inertial sensor
UR - http://dx.doi.org/10.1109/BSN.2019.8771075
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000492872400017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://ieeexplore.ieee.org/document/8771075
UR - http://hdl.handle.net/10044/1/75191
ER -