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DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION

Abstract: 

With face-recognition (FR) increasingly replacing fingerprint sensors for user-authentication on mobile devices, presentation attacks (PA) have emerged as the single most significant hurdle for manufacturers of FR systems. Current machine-learning based presentation attack detection (PAD) systems, trained in a data-driven fashion, show excellent performance when evaluated in intra-dataset scenarios. Their performance typically degrades significantly in cross-dataset evaluations. This lack of generalization in current PAD systems makes them unsuitable for deployment in real-world scenarios. Considering each dataset as representing a different domain, domain adaptation techniques have been proposed as a solution to this generalization problem. Here, we propose a novel one class domain adaptation method which uses domain guided pruning to adapt a pre-trained PAD network to the target dataset. The proposed method works without the need of collecting PAs in the target domain (i.e., with minimal information in the target domain). Experimental results on several datasets show promising performance improvements in cross-dataset evaluations.

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Paper Details

Authors:
Sushil Bhattacharjee, Sebastien Marcel
Submitted On:
15 May 2020 - 10:19am
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Amir Mohammadi
Paper Code:
4947
Document Year:
2020
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Document Files

domain_guided_pruning_icassp_2020_slides.pdf

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[1] Sushil Bhattacharjee, Sebastien Marcel, "DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5349. Accessed: Sep. 19, 2020.
@article{5349-20,
url = {http://sigport.org/5349},
author = {Sushil Bhattacharjee; Sebastien Marcel },
publisher = {IEEE SigPort},
title = {DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION},
year = {2020} }
TY - EJOUR
T1 - DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION
AU - Sushil Bhattacharjee; Sebastien Marcel
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5349
ER -
Sushil Bhattacharjee, Sebastien Marcel. (2020). DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION. IEEE SigPort. http://sigport.org/5349
Sushil Bhattacharjee, Sebastien Marcel, 2020. DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION. Available at: http://sigport.org/5349.
Sushil Bhattacharjee, Sebastien Marcel. (2020). "DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION." Web.
1. Sushil Bhattacharjee, Sebastien Marcel. DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5349