Philip Abbet

Vedrana Krivokuca Hahn, Jeremy Maceiras, Alain Komaty et al.

We present three biometric datasets (iCarB-Face, iCarB-Fingerprint, iCarB-Voice) containing face videos, fingerprint images, and voice samples, collected inside a car from 200 consenting volunteers. The data was acquired using a near-infrared camera, two fingerprint scanners, and two microphones, while the volunteers were seated in the driver's seat of the car. The data collection took place while the car was parked both indoors and outdoors, and different "noises" were added to simulate non-ideal biometric data capture that may be encountered in real-life driver recognition. Although the datasets are specifically tailored to in-vehicle biometric recognition, their utility is not limited to the automotive environment. The iCarB datasets, which are available to the research community, can be used to: (i) evaluate and benchmark face, fingerprint, and voice recognition systems (we provide several evaluation protocols); (ii) create multimodal pseudo-identities, to train/test multimodal fusion algorithms; (iii) create Presentation Attacks from the biometric data, to evaluate Presentation Attack Detection algorithms; (iv) investigate demographic and environmental biases in biometric systems, using the provided metadata. To the best of our knowledge, ours are the largest and most diverse publicly available in-vehicle biometric datasets. Most other datasets contain only one biometric modality (usually face), while our datasets consist of three modalities, all acquired in the same automotive environment. Moreover, iCarB-Fingerprint seems to be the first publicly available in-vehicle fingerprint dataset. Finally, the iCarB datasets boast a rare level of demographic diversity among the 200 data subjects, including a 50/50 gender split, skin colours across the whole Fitzpatrick-scale spectrum, and a wide age range (18-60+). So, these datasets will be valuable for advancing biometrics research.

Yong Joon Thoo, Jérémy Maceiras, Philip Abbet et al.

Robot programming methods for industrial robots are time consuming and often require operators to have knowledge in robotics and programming. To reduce costs associated with reprogramming, various interfaces using augmented reality have recently been proposed to provide users with more intuitive means of controlling robots in real-time and programming them without having to code. However, most solutions require the operator to be close to the real robot's workspace which implies either removing it from the production line or shutting down the whole production line due to safety hazards. We propose a novel augmented reality interface providing the users with the ability to model a virtual representation of a workspace which can be saved and reused to program new tasks or adapt old ones without having to be co-located with the real robot. Similar to previous interfaces, the operators then have the ability to program robot tasks or control the robot in real-time by manipulating a virtual robot. We evaluate the intuitiveness and usability of the proposed interface with a user study where 18 participants programmed a robot manipulator for a disassembly task.