Algorithm Performance Evaluation
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
Rates are based on the assumption of a threshold of 50.
Overall evaluation based on average across the three sensors
- Frej: Rate of failure to enroll
- Fcorrlive: Rate of correctly classified live fingerprints
- Fcorrfake_n: Rate of correctly classified fake fingerprints
- Ferrlive_n: Rate of misclassified live fingerprints
- Ferrfake_n: Rate of misclassified fake fingerprints
- The winner will be awarded by minimum of the average of the overall classification errors on the three sensors. Only one winner will be awarded.