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Watchlist Adaptation: Protecting the Innocent
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Datum
2020
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Gesellschaft für Informatik e.V.
Zusammenfassung
One of the most important government applications of face recognition is the watchlist
problem, where the goal is to identify a few people enlisted on a watchlist while ignoring the majority
of innocent passersby. Since watchlists dynamically change and training times can be expensive, the
deployed approaches use pre-trained deep networks only to provide deep features for face comparison.
Since these networks never specifically trained on the operational setting or faces from the watchlist,
the system will often confuse them with the faces of innocent non-watchlist subjects leading to difficult
situations, e.g., being detained at the airport to resolve their identity. We develop a novel approach
to take an existing pre-trained face network and use adaptation layers trained with our recently
developed Objectosphere loss to provide an open-set recognition system that is rapidly adapted to the
gallery while also ignoring non-watchlist faces as well as any background detections from the face
detector. While our adapter network can be quickly trained without the need of re-training the entire
representation network, it can also significantly improve the performance of any state-of-the-art face
recognition network like VGG2. We experiment with the largest open-set face recognition dataset,
the UnConstrained College Students (UCCS). It contains real surveillance camera stills including
both known and unknown subjects, as well as many non-face regions from the face detector. We
show that the Objectosphere approach is able to reduce the feature magnitude of unknown subjects as
well as background detections, so that we can apply a specifically designed similarity function on
the deep features of the Objectosphere network, which works much better than the direct prediction
of the very same network. Additionally, our approach outperforms the VGG2 baseline by a large
margin by rejecting the non-face data, and also outperforms prior state-of-the-art open-set recognition
algorithms on the VGG2 baseline data.