Can you really trust the sensor's PRNU? How image content might impact the finger vein sensor identification performance
| Autor: | Dominik Sollinger, Andreas Uhl, Luca Debiasi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
021110 strategic
defence & security studies Modality (human–computer interaction) Biometrics business.industry Computer science 0211 other engineering and technologies Image content Pattern recognition 02 engineering and technology Identification (information) Fairly bad Biometrics access control Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr48806.2021.9412194 |
| Popis: | We study the impact of highly correlated image content on the estimated photo response non-uniformity (PRNU) of a sensor unit and its impact on the sensor identification performance. Based on eight publicly available finger vein datasets, we show formally and experimentally that the nature of finger vein imagery can cause the estimated PRNU to be biased by image content and lead to a fairly bad PRNU estimate. Such bias can cause a false increase in sensor identification performance depending on the dataset composition. Our results indicate that independent of the biometric modality, examining the quality of the estimated PRNU is essential before the sensor identification performance can be claimed to be good. |
| Databáze: | OpenAIRE |
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