in-Car Biometrics (iCarB) Datasets for Driver Recognition: Face, Fingerprint, and Voice

TL;DR

This paper introduces three comprehensive in-vehicle biometric datasets for face, fingerprint, and voice, collected from 200 diverse volunteers, enabling research on driver recognition, multimodal fusion, and bias analysis in automotive settings.

Contribution

The paper presents the largest and most diverse in-vehicle biometric datasets, including the first publicly available in-vehicle fingerprint dataset, with multi-modal data for driver recognition research.

Findings

Datasets include face, fingerprint, and voice data from 200 volunteers.

Data collected under various noise and environmental conditions.

Demographic diversity enables bias and fairness studies.

Abstract

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.