TY - JOUR
T1 - FaceDate
T2 - 11th International Conference on Body Area Networks, BODYNETS 2016
AU - Neog, Pradyumna
AU - Debnath, Hillol
AU - Shan, Jianchen
AU - Paiker, Nafize R.
AU - Gehani, Narain
AU - Curtmola, Reza
AU - Ding, Xiaoning
AU - Borcea, Cristian
N1 - Funding Information:
Œis research was supported by the National Science Foundation (NSF) under Grants No. CNS 1409523, SHF 1617749, CNS 1054754, DGE 1565478, and DUE 1241976, the National Security Agency (NSA) under Grant H98230-15-1-0274, and by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) under Contract No. A8650-15-C-7521. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF, NSA, DARPA, and AFRL. Œe United States Government is authorized to reproduce and distribute reprints notwithstanding any copyright notice herein.
Publisher Copyright:
© 2017 EAI.
PY - 2017
Y1 - 2017
N2 - .is paper presents FaceDate, a novel mobile app that matches persons based on their facial looks. Each FaceDate user uploads their profile face photo and trains the app with photos of faces they like. Upon user request, FaceDate detects other users located in the proximity of the requester and performs face matching in real-time. If a mutual match is found, the two users are notied and given the option to start communicating. FaceDate is implemented over our Moitree middleware for mobile distributed computing assisted by the cloud.e app is designed to scale with the number of users, as face recognition is done in parallel at different users. FaceDate can be configured for (i) higher performance, in which case the face recognition is done in the cloud or (ii) higher privacy, in which case the face recognition is done on the mobiles.e experimental results with Android-based phones demonstrate that FaceDate achieves promising performance.
AB - .is paper presents FaceDate, a novel mobile app that matches persons based on their facial looks. Each FaceDate user uploads their profile face photo and trains the app with photos of faces they like. Upon user request, FaceDate detects other users located in the proximity of the requester and performs face matching in real-time. If a mutual match is found, the two users are notied and given the option to start communicating. FaceDate is implemented over our Moitree middleware for mobile distributed computing assisted by the cloud.e app is designed to scale with the number of users, as face recognition is done in parallel at different users. FaceDate can be configured for (i) higher performance, in which case the face recognition is done in the cloud or (ii) higher privacy, in which case the face recognition is done on the mobiles.e experimental results with Android-based phones demonstrate that FaceDate achieves promising performance.
KW - Face matching
KW - Mobile cloud app
UR - http://www.scopus.com/inward/record.url?scp=85085540620&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085540620&partnerID=8YFLogxK
U2 - 10.4108/eai.15-12-2016.2267654
DO - 10.4108/eai.15-12-2016.2267654
M3 - Conference article
AN - SCOPUS:85085540620
SN - 2310-3582
JO - BodyNets International Conference on Body Area Networks
JF - BodyNets International Conference on Body Area Networks
Y2 - 15 December 2016 through 16 December 2016
ER -