15 research outputs found
Intelligent frame selection as a privacy-friendlier alternative to face recognition
The widespread deployment of surveillance cameras for facial recognition
gives rise to many privacy concerns. This study proposes a privacy-friendly
alternative to large scale facial recognition. While there are multiple
techniques to preserve privacy, our work is based on the minimization principle
which implies minimizing the amount of collected personal data. Instead of
running facial recognition software on all video data, we propose to
automatically extract a high quality snapshot of each detected person without
revealing his or her identity. This snapshot is then encrypted and access is
only granted after legal authorization. We introduce a novel unsupervised face
image quality assessment method which is used to select the high quality
snapshots. For this, we train a variational autoencoder on high quality face
images from a publicly available dataset and use the reconstruction probability
as a metric to estimate the quality of each face crop. We experimentally
confirm that the reconstruction probability can be used as biometric quality
predictor. Unlike most previous studies, we do not rely on a manually defined
face quality metric as everything is learned from data. Our face quality
assessment method outperforms supervised, unsupervised and general image
quality assessment methods on the task of improving face verification
performance by rejecting low quality images. The effectiveness of the whole
system is validated qualitatively on still images and videos.Comment: accepted for AAAI 2021 Workshop on Privacy-Preserving Artificial
Intelligence (PPAI-21
Inverse reinforcement learning through logic constraint inference
Autonomous robots start to be integrated in human environments where explicit and implicit social norms guide the behavior of all agents. To assure safety and predictability, these artificial agents should act in accordance with the applicable social norms. However, it is not straightforward to define these rules and incorporate them in an agent's policy. Particularly because social norms are often implicit and environment specific. In this paper, we propose a novel iterative approach to extract a set of rules from observed human trajectories. This hybrid method combines the strengths of inverse reinforcement learning and inductive logic programming. We experimentally show how our method successfully induces a compact logic program which represents the behavioral constraints applicable in a Tower of Hanoi and a traffic simulator environment. The induced program is adopted as prior knowledge by a model-free reinforcement learning agent to speed up training and prevent any social norm violation during exploration and deployment. Moreover, expressing norms as a logic program provides improved interpretability, which is an important pillar in the design of safe artificial agents, as well as transferability to similar environments
Werkgelegenheidsconferentie federale regering : zes voorstellen van werkexperten uit verschillende disciplines
Voorstel 1. Laat partners aan elkaar pensioenrechten doneren (prof.
dr. Luc Van Ootegem en prof. dr. Elsy Verhofstadt)
Voorstel 2. Schrap loonlastverminderingen voor oudere werknemers
(prof. dr. Bart Cockx en dr. Sam Desiere)
Voorstel 3. Verlaag pensioenopbouw bij langdurige werkloosheid
(prof. dr. Stijn Baert)
Voorstel 4. Voer deeltijds pensioen pas in na grondig studiewerk (om
averechtse effect te vermijden) (prof. dr. Bart Cockx)
Voorstel 5. Zorg ervoor dat ouder wordende werknemers met fysiek
actieve jobs hun inspanningen zelf meer kunnen controleren (prof. dr.
Els Clays)
Voorstel 6. Gebruik wetenschappelijk onderbouwde tools om oudere
werknemers te heroriënteren (prof. dr. Bart Wille en prof. dr. Filip De
Fruyt