Verification system based on long-range iris and Graph Siamese Neural Networks
| dc.contributor.author | Zola, Francesco | |
| dc.contributor.author | Fernandez-Carrasco, José Álvaro | |
| dc.contributor.author | Bruse, Jan Lukas | |
| dc.contributor.author | Galar Idoate, Mikel | |
| dc.contributor.author | Geradts, Zeno | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.date.accessioned | 2023-09-08T08:29:48Z | |
| dc.date.available | 2023-09-08T08:29:48Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-09-08T08:23:56Z | |
| dc.description.abstract | Biometric systems represent valid solutions in tasks like user authentication and verification, since they are able to analyze physical and behavioural features with high precision. However, especially when physical biometrics are used, as is the case of iris recognition, they require specific hardware such as retina scanners, sensors, or HD cameras to achieve relevant results. At the same time, they require the users to be very close to the camera to extract high-resolution information. For this reason, in this work, we propose a novel approach that uses long-range (LR) distance images for implementing an iris verification system. More specifically, we present a novel methodology for converting LR iris images into graphs and then use Graph Siamese Neural Networks (GSNN) to predict whether two graphs belong to the same person. In this study, we not only describe this methodology but also evaluate how the spectral components of these images can be used for improving the graph extraction and the final classification task. Results demonstrate the suitability of this approach, encouraging the community to explore graph application in biometric systems. | en |
| dc.description.sponsorship | This work has been partially supported by the Spanish Centre for the Development of Industrial Technology (CDTI) under the project ÉGIDA (EXP 00122721 / CER-20191012) - RED DE EXCELENCIA EN TECNOLOGIAS DE SEGURIDAD Y PRIVACIDAD | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Zola, F., Fernandez-Carrasco, J. A., Bruse, J. L., Galar, M., Geradts, Z. (2022) Verification system based on long-range iris and Graph Siamese Neural Networks. En [ACM], ESSE '22: Proceedings of the 2022 European Symposium on Software Engineering (pp. 80-88). Association for Computing Machinery. https://doi.org/10.1145/3571697.3571708. | en |
| dc.identifier.doi | 10.1145/3571697.3571708 | |
| dc.identifier.isbn | 978-1-4503-9730-8 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/46254 | |
| dc.language.iso | eng | en |
| dc.publisher | ACM | en |
| dc.relation.ispartof | ACM. ESSE '22: Proceedings of the 2022 European Symposium on Software Engineering: Association for Computing Machinery; 2022. p.80-88 978-1-4503-9730-8 | en |
| dc.relation.publisherversion | https://doi.org/10.1145/3571697.3571708 | |
| dc.rights | © 2022 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ESSE '22: Proceedings of the 2022 European Symposium on Software Engineering, https://doi.org/10.1145/3571697.3571708. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Graph Siamese Network | en |
| dc.subject | Iris recognition | en |
| dc.subject | Long-range iris | en |
| dc.subject | Verification system | en |
| dc.title | Verification system based on long-range iris and Graph Siamese Neural Networks | en |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 44c7a308-9c21-49ef-aa03-b45c2c5a06fd | |
| relation.isAuthorOfPublication.latestForDiscovery | 44c7a308-9c21-49ef-aa03-b45c2c5a06fd |