Damer, Naser ; Terhörst, Philipp ; Braun, Andreas ; Kuijper, Arjan (2017)
Indexing of Single and Multi-instance Iris Data Based on LSH-Forest and Rotation Invariant Representation.
7th international Conference on Computer Analysis of Images and Patterns (CAIP 2017). Ystad, Sweden (August 22.-24., 2017)
doi: 10.1007/978-3-319-64698-5_17
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Indexing of iris data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by the tradeoffs between accuracy, computational efficacy, storage costs, and maintainability. This work presents an iris indexing approach based on rotation invariant iris representation and LSH-Forest to produce an accurate and easily maintainable indexing structure. The complexity of insertion or deletion in the proposed method is limited to the same logarithmic complexity of a query and the required storage grows linearly with the database size. The proposed approach was extended into a multi-instance iris indexing scheme resulting in a clear performance improvement. Single iris indexing scored a hit rate of 99.7% at a 0.1% penetration rate while multi-instance indexing scored a 99.98% hit rate at the same penetration rate. The evaluation of the proposed approach was conducted on a large database of 50k references and 50k probes of the left and the right irises. The advantage of the proposed solution was put into prospective by comparing the achieved performance to the reported results in previous works.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Damer, Naser ; Terhörst, Philipp ; Braun, Andreas ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Indexing of Single and Multi-instance Iris Data Based on LSH-Forest and Rotation Invariant Representation |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Veranstaltungstitel: | 7th international Conference on Computer Analysis of Images and Patterns (CAIP 2017) |
| Veranstaltungsort: | Ystad, Sweden |
| Veranstaltungsdatum: | August 22.-24., 2017 |
| DOI: | 10.1007/978-3-319-64698-5_17 |
| URL / URN: | https://link.springer.com/chapter/10.1007%2F978-3-319-64698-... |
| Kurzbeschreibung (Abstract): | Indexing of iris data is required to facilitate fast search in large-scale biometric systems. Previous works addressing this issue were challenged by the tradeoffs between accuracy, computational efficacy, storage costs, and maintainability. This work presents an iris indexing approach based on rotation invariant iris representation and LSH-Forest to produce an accurate and easily maintainable indexing structure. The complexity of insertion or deletion in the proposed method is limited to the same logarithmic complexity of a query and the required storage grows linearly with the database size. The proposed approach was extended into a multi-instance iris indexing scheme resulting in a clear performance improvement. Single iris indexing scored a hit rate of 99.7% at a 0.1% penetration rate while multi-instance indexing scored a 99.98% hit rate at the same penetration rate. The evaluation of the proposed approach was conducted on a large database of 50k references and 50k probes of the left and the right irises. The advantage of the proposed solution was put into prospective by comparing the achieved performance to the reported results in previous works. |
| Freie Schlagworte: | Biometrics, Multibiometrics, Iris recognition, Indexing, CRISP |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 04 Mai 2020 12:27 |
| Letzte Änderung: | 04 Mai 2020 12:27 |
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