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Watchlist Adaptation: Protecting the Innocent


Günther, Manuel; Dhamija, Akshay Raj; Boult, Terrance E (2020). Watchlist Adaptation: Protecting the Innocent. In: International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 16 September 2020 - 18 September 2020, 1-7.

Abstract

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 retraining 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.

Abstract

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 retraining 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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:Open-Set Face Recognition Watchlist Gallery Adaptation
Language:English
Event End Date:18 September 2020
Deposited On:13 Oct 2020 10:37
Last Modified:27 Nov 2020 07:34
Publisher:IEEE
ISBN:978-3-88579-700-5
OA Status:Green
Official URL:https://ieeexplore.ieee.org/document/9210977
Related URLs:https://dl.gi.de/handle/20.500.12116/34338 (Publisher)

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Content: Published Version
Language: English
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Licence: Creative Commons: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)