A python package for biometric and binary classification systems performance evaluation
Project description
PyEER is a python package for biometric systems performance evaluation. Includes ROC, DET, FNMR, FMR and CMC curves plotting, scores distribution plotting, EER and operating points estimation. It can be also used to evaluate binary classification systems.
Two programs are provided within this package:
geteerinf: Receive two files holding genuine match scores and impostor match scores [1]. Genuine match scores are obtained by matching feature sets of the same class (same person), while impostor matching scores are obtained by matching feature sets of different classes (different persons). Using these scores the program plots ROC, DET, FNMR(t), FMR(t) curves and estimates Equal Error Rate Value and operating points for each system. EER values are reported as specified in [2]
getcmcinf: Receive two files holding match scores and genuine query-template pairs [1]. This program is provided to evaluate biometrics systems in identification scenarios. Using the scores provided, CMC curves and rank values for each score file are reported.
Utilities provided within this package can also be used to develop other scripts by importing the module pyeer.
PyEER has been developed with the idea of providing researchers and the scientific community in general with a tool to correctly evaluate and report the performance of their systems.
Installing
pip install pyeergeteerinf input file formats
Genuine match scores must be provided in a file with one score per line. Each line can have any number of columns but the scores must be in the last column. For impostor match scores the program can handle two different formats:
Histogram format
Although the vast majority of the systems report scores normalized between 0 and 1, there are some that report integer scores [3]. When computing a lot of impostor scores, millions of them, it can be computationally expensive to read all those scores from a file. Therefore, in those cases may be worth it to use this format.
Restrictions: Only integer scores are supported
File format: Each line contains the number of scores equals to the index of the line in the file (starting from zero). For example, given a file:
The above file example indicates that there are 123 scores equals to 0, 12 scores equals to 1, 212 scores equals to 2, 321 scores equals to 3 and so on.
Recommendations: Use this format for very large experiments (millions of scores).
Note: Only impostor scores file can mimic this format.
Non-Histogram format
Restrictions: None. Integer and float scores are both supported.
File format: All the scores one by line, just as the genuine match scores file format
getcmcinf input file formats
Scores file
Each line must have the following format: (query template score)
Genuine query-template pairs
Each line must have the following format: (query corresponding_template)
Usage
console cmd: geteerinf console cmd: getcmcinf
Examples:
To print the script help
geteerinf -hOne experiment (Non-histogram format):
geteerinf -p "example_files/non_hist/" -i "exp3_false.txt" -g "exp3_true.txt" -e "exp3"More than one experiment (Non-histogram format):
geteerinf -p "example_files/non_hist/" -i "exp1_false.txt,exp2_false.txt" -g "exp1_true.txt,exp2_true.txt" -e "exp1,exp2"One experiment (Histogram format):
geteerinf -p "example_files/hist/" -i "exp1_false.txt" -g "exp1_true.txt" -e "exp1" -htMore than one experiment (Identification experiment):
getcmcinf -p "example_files/cmc/" -ms "exp1_scores.txt,exp2_scores.txt" -t "exp1_tp.txt,exp2_tp.txt" -e "Exp1,Exp2"For all the above examples a CSV file will be generated in the directory where the program was invoked. The CSV file will include all the calculated stats. To specify the directory where to saved it, you can use the “-sp” option.
Note: To run the above examples you can download the score files from the project site on Gitlab or extract them from inside the package installation
Contributing
Do you find PyEER useful? You can collaborate with us:
References
[1] D. Maltoni et al., Handbook of Fingerprint Recognition, Springer-Verlag London Limited 2009
[2] Maio D., Maltoni D., Cappelli R., Wayman J.L. and Jain A.K., “FVC2000: Fingerprint verification competition,” IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 24, no. 3, pp. 402–412, 2002
[3] Hernandez-Palancar, J., Munoz-Briseno, A., & Gago-Alonso, A. (2014). Using a triangular matching approach for latent fingerprint and palmprint identifica- tion. International Journal of Pattern Recognition and Artificial Intelligence, 28, 1460004.
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