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

Citation Author(s):
Sushil Bhattacharjee, Sebastien Marcel
Submitted by:
Amir Mohammadi
Last updated:
15 May 2020 - 10:19am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Amir Mohammadi
Paper Code:
4947
Categories:

Abstract 

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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domain_guided_pruning_icassp_2020_slides.pdf

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