ZBUM - Biometrics and Machine Learning Group Site - Biometrics and Machine Learning Group

  • About
  • Group
    • A. Pacut
    • M. Trokielewicz
    • W. Gutfeter
    • E. Bartuzi
    • K. Roszczewska
    • K. Gabor
    • M. Hałoń
    • M. Azimi
    • J. N. Khirak
  • Projects
    • AMBER
    • BIOWIZ
    • yieldPlanet
  • Research
    • Databases
    • Laboratory
  • Teaching
    • Biometrics
    • CV
    • NN
  • Scientific Club
  • PhD Theses
  • Publications
    • Biometrics
    • ML

Biometrics and Machine Learning Group

Home

Latest news

We are pleased to announce that Mateusz Trokielewicz defended (with honors) his doctoral dissertation entitled „Iris Recognition Methods Resistant to Biological Changes in the Eye” , supervised by prof. Czajka and prof. Pacut, on the 18th of July, 2019.

Iris scanner can distinguish dead eyeballs from living ones: MIT Technology Review reports on our recent developements in the field of presentation attack detection for cadaver irises.

We are pleased to announce that Mateusz Trokielewicz received the EAB European Biometrics Research Award 2016 for research on iris recognition reliability including template aging, influence of eye diseases and post-mortem recognition.

Is That Eyeball Dead or Alive? Adam Czajka discusses the prevention of iris sensors accepting the use of a high-resolution photo of an iris or, in a grislier scenario, an actual eyeball. For full article, please see IEEE Spectrum.

wersja polska

Facial identification algorithm for International Challenge on Biometric Recognition in the Wild

The algorithm described in the paper "Deep Neural Network ensembles dedicated to different head poses for face identification" was proposed to resolve the task of the of the ICB-RW 2016 challenge (International Challenge on Biometric Recognition in the Wild).

Probe images from ICBRW dataset.

The main objective of the competition was to identify the people in the CCTV surveillance system assuming that the watch-list with good quality images was provided. Training dataset, which was delivered by the organizers, contained both the watch-list images of 90 subjects (called there the gallery subset) and the probe images which are the static frames obtained from the surveillance system. Each subject in in the gallery subset was represented by the 3 images with different poses (frontal, left-side and right-side) The probe images was captured outdoor, subjects rarely look into the camera that was localized over their heads and faces are often occluded.
The results of ICB-RW Challenge were evaluated using an identification index given by the area under curve (AUC) calculated for the cumulative match score (CMC). The proposed algorithm took fourth place in the challenge.

Challenge evaluation results.

Method was based on deep neural networks ensembles. Each ensemble network was trained to classify faces seen from the specific directions, namely, frontal images, left and right profiles. This approach meant to reflect the given watch list structure. Results were then combined to obtain the final classifier.

Detailed description in papers:

  1. J Neves, H Proença, "ICB-RW 2016: International challenge on biometric recognition in the wild", Biometrics (ICB), 2016 International Conference on, 1-6
  2. H. Proença, M. Nixon, M. Nappi, E. Ghaleb, G. Özbulak, H. Gao, H. K. Ekenel, K. Grm, V. Struc, H. Shi, X. Zhu, S. Liao, Z. Lei, S. Z. Li, W. Gutfeter, A. Pacut, J. Brogan, W. J. Scheirer, E. Gonzalez-Sosa, R. Vera-Rodriguez, J. Fierrez, J. Ortega-Garcia, D. Riccio, L. De Maio, "Trends and Controversies", IEEE Intelligent Systems, vol. 33, no. 3, pp. 41-67, May./Jun. 2018.

Contact us

Institute of Control
and Computation Engineering

Warsaw University of Technology
ul. Nowowiejska 15/19
00-665 Warsaw, Poland
phone: +48 (22) 234 73 97

Logowanie

  • Request new password
  • about
  • group
  • projects
  • research
  • teaching
  • scientific club
  • phd theses
  • publications