PyEER is a python package intended for biometric systems performance evaluation but it can be used to evaluate binary classification systems also. It 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.
Within this package, two command line programs are provided:
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geteerinf: Useful to evaluate biometrics systems in verification scenarios as well as binary classification systems.
-
getcmcinf: Useful to evaluate biometrics systems in identification scenarios.
Utilities provided within this package can also be used to develop other scripts by importing the module pyeer.
To install the PyEER package you only need to type into a terminal the following line:
pip install pyeer
Using PyEER is very easy and straightforward. Don't you believe? Let's see if we can convince you.
When evaluating biometric systems in verification scenarios some researchers will only report Equal Error Rate (EER) values to show the accuracy of their proposals. However, this is wrong. The behavior of biometric systems cannot be fully assessed in this way. In other to achieve that, Receiver Operating Characteristic (ROC) or Detection Error Tradeoff (DET) graph must be reported. It is also very common to report other operating points besides EER (i.e. MR100, FMR1000, ZeroFMR, and ZeroFNMR). geteerinf provides these and more. You will only need to provide 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).
Genuine match scores and impostor match scores must be provided in separated files one score per line. Each line can have any number of columns but the scores must be in the last column. Additionally, impostor match scores can be provided in a different format which explained next
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).
Recommendations: Use this format for very large experiments (millions of scores).
If you have any doubts left, you should check the example files on GitHub.
geteerinf -h
geteerinf -p "example_files/non_hist/" -i "exp3_false.txt" -g "exp3_true.txt" -e "exp3"
geteerinf -p "example_files/non_hist/" -i "exp1_false.txt,exp2_false.txt" -g "exp1_true.txt,exp2_true.txt" -e "exp1,exp2"
geteerinf -p "example_files/hist/" -i "exp1_false.txt" -g "exp1_true.txt" -e "exp1" -ht
All of the above examples will generate the following information:
- Graphs:
- Receiver operating characteristic (ROC)
- Detection error tradeoff (DET)
- False Match Rate (FMR) and False Non-Match Rate(FNMR)
- Genuine and impostor score histograms
- pyeer_report.csv:
- Genuine scores mean
- Genuine sores standard deviation
- Impostor scores mean
- Impostor sores standard deviation
- Sensitivity index (d') (See NICE:II protocol evaluation)
- Equal Error Rate (EER) (EER values are reported as specified in [2]).
- Area under the ROC curve
- Youden's J statistic (Youden's Index)
- Youden's Index threshold
- Matthews Correlation Coefficient (MCC)
- Matthews Correlation Coefficient threshold
- Equal Error Rate (EER) (EER values are reported as specified in [2]).
- ZeroFMR, FMR1000, FMR100, FMR20, FMR10, and ZeroFNMR
- Equal Error Rate threshold
- ZeroFMR, FMR1000, FMR100, FMR20, FMR10, and ZeroFNMR thresholds
- rates.csv:
- False Match Rates
- False Non-Match Rates
from pyeer.eer_info import get_eer_stats
from pyeer.report import generate_eer_report, export_error_rates
from pyeer.plot import plot_eer_stats
# Calculating stats for classifier A
stats_a = get_eer_stats(gscores_a, iscores_a)
# Calculating stats for classifier B
stats_b = get_eer_stats(gscores_b, iscores_b)
# Generating CSV report
generate_eer_report([stats_a, stats_b], ['A', 'B'], 'pyeer_report.csv')
# Generating HTML report
generate_eer_report([stats_a, stats_b], ['A', 'B'], 'pyeer_report.html')
# Generating Latex report
generate_eer_report([stats_a, stats_b], ['A', 'B'], 'pyeer_report.tex')
# Generating JSON report
generate_eer_report([stats_a, stats_b], ['A', 'B'], 'pyeer_report.json')
# Exporting error rates (Exporting FMR and FNMR to a CSV file)
# This is the DET curve, the ROC curve is a plot of FMR against 1 - FNMR
export_error_rates(stats_a.fmr, stats_a.fnmr, 'A_DET.csv')
export_error_rates(stats_b.fmr, stats_b.fnmr, 'B_DET.csv')
# Plotting
plot_eer_stats([stats_a, stats_b], ['A', 'B'])
In identification experiments in closed sets sometimes only rank values are reported [1]. To obtain rank values and the Cumulative match curve (CMC) getcmcinf is provided. It receives the match scores and genuine correspondences. The input format will be described next.
Input files must have the following formats:
-
Match scores: Each line must have the following format: (query template score)
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Genuine query-template pairs: Each line must have the following format: (query corresponding_template)
For more clarification, you should check the example files on GitHub.
getcmcinf -h
getcmcinf -p "example_files/cmc/" -ms "exp1_scores.txt" -t "exp1_tp.txt" -e "Exp1"
getcmcinf -p "example_files/cmc/" -ms "exp1_scores.txt,exp2_scores.txt" -t "exp1_tp.txt,exp2_tp.txt" -e "Exp1,Exp2"
All of the above examples will generate the following information:
-
Graphs:
- Cumulative match curve (CMC)
-
pyeer_report.csv:
- Rank identification rates
from pyeer.cmc_stats import load_scores_from_file, get_cmc_curve, CMCstats
from pyeer.report import generate_cmc_report
from pyeer.plot import plot_cmc_stats
# CMC maximum rank
r = 20
# Loading scores
sfile = 'cmc/exp1_scores.txt' # The scores file
tp_file = 'cmc/exp1_tp.txt' # The genuine pairs relationship in "sfile"
scores = load_scores_from_file(sfile, tp_file)
# Calculating CMC curve
ranks = get_cmc_curve(scores, r)
# Creating stats
stats = [CMCstats(exp_id='A', ranks=ranks)]
# Generating CSV report
generate_cmc_report(stats, r, 'pyeer_report.csv')
# Generating HTML report
generate_cmc_report(stats, r, 'pyeer_report.html')
# Generating Latex report
generate_cmc_report(stats, r, 'pyeer_report.tex')
# Generating Latex report
generate_cmc_report(stats, r, 'pyeer_report.json')
# Plotting
plot_cmc_stats(stats, r)
Do you find PyEER useful? You can collaborate with us:
Please follow our contributing guidelines.
We would like to thanks to those who has collaborated with the project:
- ljsoler
- Added support in CMC stats for multiple templates for a single query
- jpalancar
- Testing and guidance
[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 identification. International Journal of Pattern Recognition and Artificial Intelligence, 28, 1460004.