Algorithm Performance Evaluation
The parameters adopted for the performance evaluation will be the following:
Evaluation per dataset
- Frej_n: Rate of failure to process for the sub-set n
- Fcorrlive_n: Rate of correctly classified live iris for sub-set n
- Fcorrfake_n: Rate of correctly classified fake iris for sub-set n
- Ferrlive_n: Rate of misclassified live iris for sub-set n
- Ferrfake_n: Rate of misclassified fake iris for sub-set n
- ET: Average processing time per image
Rates are based on the assumption of a threshold of 50.
Overall evaluation based on average across the three datasets
- Frej: Rate of failure to process
- Fcorrlive: Rate of correctly classified live iris
- Fcorrfake_n: Rate of correctly classified fake iris
- Ferrlive_n: Rate of misclassified live iris
- Ferrfake_n: Rate of misclassified fake iris
- The winner will be awarded by minimum of the average of the overall classification errors on all datasets. Only one winner will be awarded.