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

Vedrana Krivokuca Hahn, Jeremy Maceiras, Alain Komaty, Philip Abbet, Sebastien Marcel

TL;DR

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+).

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.

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

TL;DR

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+).

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.

Paper Structure

This paper contains 14 sections, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Data Collection Methodology
  3. Biometric sensor set-up
  4. Data collection protocols
  5. Data Description
  6. iCarB-Face
  7. iCarB-Fingerprint
  8. iCarB-Voice
  9. Using the provided evaluation protocols
  10. Limitations
  11. iCarB-Face
  12. iCarB-Fingerprint
  13. iCarB-Voice
  14. Ethics Statement

Figures (14)

  • Figure 1: Biometric sensor set-up inside the car. The car cockpit was generated by Artificial Intelligence, as an NDA with our industrial partner prevents us from showing images of the sensor set-up inside the real test vehicle.
  • Figure 2: MSC2-M42-1-A camera and the custom NIR illuminator circuits.
  • Figure 3: Fingerprint scanners: (a) OYSTER III; (b) CSD101i.
  • Figure 4: Microphones: (a) ME72; (b) AT2020.
  • Figure 5: Schematic of the circuit connecting the ME72 microphone to a battery and a laptop.
  • ...and 9 more figures