The Department of Electrical and Electronic Engineering of the University of Cagliari is proud to announce the seventh edition of the Fingerprint Liveness Detection Competition.

Spoofing - The widespread use of personal verification systems based on fingerprints has shown some weaknesses related to the problem of security. Among the others, it is well-known that a fingerprint verification system can be deceived by submitting artificial reproductions of fingerprints made up of silicon or gelatine to the electronic capture device (optical, capacitive, etc...). These images are then processed as “true” fingerprints.

Liveness Detection - Therefore, a recent issue in the field of security in fingerprint verification (unsupervised especially) is known as “liveness detection”. The standard verification system is coupled with additional hardware or software modules aimed to certify the authenticity of the submitted fingerprints. Whilst hardware-based solutions are the most expensive, software-based ones attempt to measure liveness from characteristics of images themselves by simply applying image processing algorithms.

Software Liveness Classification - The problem of vitality detection is treated as a two-class classification problem (live or fake). An appropriate classifier is designed in order to extract the probability of the image vitality given the extracted set of features. LivDet2021 competition is open to all academic and industrial institutions which have a solution for software-based fingerprint recognition and liveness detection. 

LivDet 2021 Competition Overview - This edition of LivDet 2021 has two challenges.

  • Liveness Detection in Action(1): Fingerprint Liveness Detection systems are not designed to operate stand-alone, but as a part of a recognition system. Competitors are invited to submit a complete algorithm able not only to output the probability of the image vitality (the so-called “liveness score”) given the extracted set of features but also an integrated match score (“integrated score”) which includes the probability above with the probability of belonging to the declared user. For this challenge, participants can decide whether to exploit the additional information coming from the enrolled user (“user-speciļ¬c effect”(2)).
  • Fingerprint representation: In modern biometric systems, the compactness and the discriminability of feature vectors are fundamental to guarantee high performance in terms of accuracy and speed. Competitors are invited to submit a liveness detection algorithm which returns in addition to the probability of liveness, the feature vector corresponding to the input image. The algorithms will be assessed on the basis of system accuracy, accuracy using the extracted features and a linear SVM and feature compactness.

Each participant is invited to submit its algorithm in a Win32 or Linux console application. The performance will be evaluated by utilizing a very large data set of “fake” and “live” fingerprint images captured by three devices. The performance rank will be compiled and published in this site.

The goal of the competition is to compare different methodologies for software-based fingerprint liveness detection with a common experimental protocol and data set. The ambition of the competition is to become the reference event for academic and industrial research. The competition is not defined as an official system for quality certification of the proposed solutions, but may impact state-of-art in this crucial field, with reference to the general problem of security in biometric systems.

LivDet 2019 Results The preprint version of the ICB 2019 paper is available on: https://arxiv.org/abs/1905.00639.


IMPORTANT DATES
Deadlines:

  • Registration Opening: February 21, 2020.
  • Registration Deadline: July 15, 2020.
  • Algorithm submission: December 31, 2020.

The training-set will be made available during the registration period.


(1) The "Liveness Detection in Action" name is inspired by "Anti-spoofing in Action: Joint Operation with a Verification System, I. Chingovska, A. Anjos and S. Marcel,  2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, 2013, pp. 98-104. doi: 10.1109/CVPRW.2013.22"

(2) Ghiani, L., Marcialis, G.L., Roli, F., Tuveri, P.: User-specific effects in fingerprint presentation attacks detection: insights for future research. In: 2016 ICB, Halmstad, pp. 1–6 (2016). doi: 10.1109/ICB.2016.7550081


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