private static BiometricResult Evaluate(ClassifierInstance classifier, List <ISample> testingSamples, double minInterval, double interval, BiometricResult result)
{
if (interval < minInterval)
{
// the threshold found for the smallest interval represents the best guess at the EER
return(result);
}
else
{
// recurse until the smallest allowable interval is hit
ErrorRatePair delta1 = CalculateErrorRate(classifier, testingSamples, (result.GetThreshold() - (interval / 2.0)));
ErrorRatePair delta2 = CalculateErrorRate(classifier, testingSamples, (result.GetThreshold() + (interval / 2.0)));
Console.WriteLine("delta1 : " + delta1.GetFAR() + " " + delta1.GetFRR() + " \n " + delta2.GetFAR() + " " + delta2.GetFRR());
Console.WriteLine("threshold: " + result.GetThreshold());
Console.WriteLine("errorDelta1: " + delta1.GetErrorDelta() + " errorDelta2: " + delta2.GetErrorDelta());
if (delta1.GetErrorDelta() < delta2.GetErrorDelta())
{
Console.WriteLine("Choose delta1");
return(Evaluate(classifier, testingSamples, minInterval, (interval / 2.0), new BiometricResult((result.GetThreshold() - (interval / 2.0)), new List <ErrorRatePair> {
delta1
})));
}
else
{
Console.WriteLine("Choose delta2");
return(Evaluate(classifier, testingSamples, minInterval, (interval / 2.0), new BiometricResult((result.GetThreshold() + (interval / 2.0)), new List <ErrorRatePair> {
delta2
})));
}
}
}
public static ErrorRatePair CalculateErrorRate(ClassifierInstance classifier, List <ISample> testingSamples, double threshold)
{
Tuple <int, int> farResults = CalculateFAR(classifier, testingSamples, threshold);
Tuple <int, int> frrResults = CalculateFRR(classifier, testingSamples, threshold);
return(new ErrorRatePair(farResults.Item1, farResults.Item2, frrResults.Item1, frrResults.Item2));
}
private static Tuple <int, int> CalculateFRR(ClassifierInstance classifier, List <ISample> testingSamples, double threshold)
{
string[] identifiers = classifier.GetTrainedIdentifiers();
// for each identifier find the samples taht incorrectly match the identifier given the threshold
VerificationResult result = identifiers.AsParallel().SelectMany(identifier =>
testingSamples.Where(sample => sample.GetIdentifier().Equals(identifier)).Select(sample =>
{
if (classifier.Verify(sample.GetDimensions(), identifier) <= threshold)
{
return(new VerificationResult(0, 1)); // falsely rejected sample for the correct identifier
}
else
{
return(new VerificationResult(1, 0)); // correctly accepted sample for the identifier
}
})
).Aggregate((current, next) => current + next);
return(new Tuple <int, int>(result.Rejected, (result.Accepted + result.Rejected)));
}
// perform EER evaluation on provided testing samples
public static BiometricResult Evaluate(ClassifierInstance classifier, List <ISample> testingSamples, double minInterval = DEFAULT_MIN_INTERVAL)
{
return(Evaluate(classifier, testingSamples, minInterval, 0.5, new BiometricResult(0.5, null)));
}
