The parameters adopted for the performance evaluation will be the following:

  • Evaluation per sensor sub-set
    • Frej_n: Rate of failure to enroll for the sub-set n.
    • Fcorrlive_n: Rate of correctly classified live fingerprints for sub-set n.
    • Fcorrfake_n: Rate of correctly classified fake fingerprints for sub-set n.
    • Ferrlive_n: Rate of misclassified live fingerprints for sub-set n.
    • Ferrfake_n: Rate of misclassified fake fingerprints for sub-set n.
    • ET: Average processing time per image.
    • MAM: Max Allocated Memory while the algorithm is running.
  • Overall evaluation
    • Frej: Rate of failure to enroll.
    • Fcorrlive: Rate of correctly classified live fingerprints.
    • Fcorrfake: Rate of correctly classified fake fingerprints.
    • Ferrlive: Rate of misclassified live fingerprints.
    • Ferrfake: Rate of misclassified fake fingerprints.

Scores [0, 50) classify fingerprint image as "fake" while scores [50,100] classify fingerprint image as "live".

The winner will be awarded by simple averaging the overall classification errors on the datasets.