Boutros, Fadi ; Kaehm, Olga ; Fang, Meiling ; Kirchbuchner, Florian ; Damer, Naser ; Kuijper, Arjan (2022)
Low-resolution Iris Recognition via Knowledge Transfer.
21st International Conference of the Biometrics Special Interest Group (BIOSIG). Darmstadt, Germany (14.-16.09.2022)
doi: 10.1109/BIOSIG55365.2022.9896959
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
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 low-resolution 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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Boutros, Fadi ; Kaehm, Olga ; Fang, Meiling ; Kirchbuchner, Florian ; Damer, Naser ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Low-resolution Iris Recognition via Knowledge Transfer |
| Sprache: | Englisch |
| Publikationsjahr: | 27 September 2022 |
| Ort: | Piscataway, NJ |
| Verlag: | Gesellschaft für Informatik e.V. |
| Buchtitel: | BIOSIG 2022: Proceedings of the 21st International Conference of the Biometrics Special Interest Group |
| Reihe: | Lecture Notes in Informatics |
| Band einer Reihe: | 329 |
| Veranstaltungstitel: | 21st International Conference of the Biometrics Special Interest Group (BIOSIG) |
| Veranstaltungsort: | Darmstadt, Germany |
| Veranstaltungsdatum: | 14.-16.09.2022 |
| DOI: | 10.1109/BIOSIG55365.2022.9896959 |
| Kurzbeschreibung (Abstract): | 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 low-resolution 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. |
| Freie Schlagworte: | Iris recognition, Biometrics, Deep learning, Machine learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 24 Nov 2022 08:37 |
| Letzte Änderung: | 21 Mär 2023 07:42 |
| PPN: | 506174492 |
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