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Utility-based performance evaluation of biometric sample quality assessment algorithms
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Datum
2022
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Gesellschaft für Informatik e.V.
Zusammenfassung
The quality score of a biometric sample is expected to predict the sample’s utility, but a
universally valid definition of utility is missing. A harmonized definition of utility would be useful to
facilitate the comparison of biometric sample quality assessment algorithms. This paper generalizes
the utility of a biometric sample as normalized difference between the means of non-mated and
mated comparison scores with respect to this sample. Using a face image data set, we show that
discarding samples with low utility scores determined in this way results in a rapidly declining false
non-match rate. The obtained utility scores can be used as ground-truth utility labels for training
biometric sample quality assessment algorithms and for summarizing their prediction performance
in a single plot and in a single figure of merit based on the proposed utility score definition.