Konferenzbeitrag
Low-resolution Iris Recognition via Knowledge Transfer
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Dokumententyp
Text/Conference Paper
Datum
2022
Autor:innen
Zeitschriftentitel
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Bandtitel
Quelle
BIOSIG 2022
Further Conference Contributions
Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
This work introduces a novel approach for extremely low-resolution iris recognition based
on deep knowledge transfer. This work starts by adapting the penalty margin loss to the iris recognition
problem. This included novel analyses on the appropriate penalty margin for iris recognition.
Additionally, this work presents analyses toward finding the optimal deeply learned representation
dimension for the identity information embedded in the iris capture. Most importantly, this work
proposes a training framework that aims at producing iris deep representations from extremely lowresolution
that are similar to those of high resolution. This was realized by the controllable knowledge
transfer of an iris recognition model trained for high-resolution images into a model that is
specifically trained for extremely low-resolution irises. The presented approach leads to the reduction
of the verification errors by more than 3 folds, in comparison to the traditionally trained model
for low-resolution iris recognition.