TY - GEN
T1 - Live face de-identification in video
AU - Gafni, Oran
AU - Wolf, Lior
AU - Taigman, Yaniv
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having the perception (pose, illumination and expression) fixed. We achieve this by a novel feed-forward encoder-decoder network architecture that is conditioned on the high-level representation of a person's facial image. The network is global, in the sense that it does not need to be retrained for a given video or for a given identity, and it creates natural looking image sequences with little distortion in time.
AB - We propose a method for face de-identification that enables fully automatic video modification at high frame rates. The goal is to maximally decorrelate the identity, while having the perception (pose, illumination and expression) fixed. We achieve this by a novel feed-forward encoder-decoder network architecture that is conditioned on the high-level representation of a person's facial image. The network is global, in the sense that it does not need to be retrained for a given video or for a given identity, and it creates natural looking image sequences with little distortion in time.
UR - http://www.scopus.com/inward/record.url?scp=85081914056&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2019.00947
DO - 10.1109/ICCV.2019.00947
M3 - פרסום בספר כנס
AN - SCOPUS:85081914056
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 9377
EP - 9386
BT - Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 October 2019 through 2 November 2019
ER -