Iris recognition algorithms have recently demonstrated excellent performance in the authentication task. In this paper, we present a technology transfer project for the development and testing of a biometric recognition system under challenging operational conditions. Due to the stringent operational requirements, the design and implementation of the system included a phase of selecting technologically advanced hardware. The lack of corresponding data sets implied a novel acquisition step. The evaluation phase is preliminary as the data set is being expanded for the acquisition of new samples capable of highlighting the system’s critical issues. Current samples were acquired in very different lighting conditions and in the presence of glasses, which was not yet done in the literature. In addition to the selected hardware, such data allowed us to simulate a realistic environmental context for the project’s final prototype.

Preliminary Results on a "Real" Iris Recognition System under Challenging Operational Conditions

Sara Concas
;
Giulia Orru';Gian Luca Marcialis
2023-01-01

Abstract

Iris recognition algorithms have recently demonstrated excellent performance in the authentication task. In this paper, we present a technology transfer project for the development and testing of a biometric recognition system under challenging operational conditions. Due to the stringent operational requirements, the design and implementation of the system included a phase of selecting technologically advanced hardware. The lack of corresponding data sets implied a novel acquisition step. The evaluation phase is preliminary as the data set is being expanded for the acquisition of new samples capable of highlighting the system’s critical issues. Current samples were acquired in very different lighting conditions and in the presence of glasses, which was not yet done in the literature. In addition to the selected hardware, such data allowed us to simulate a realistic environmental context for the project’s final prototype.
2023
Biometrics; Iris; Iris recognition
File in questo prodotto:
File Dimensione Formato  
paper13.pdf

accesso aperto

Tipologia: versione editoriale (VoR)
Dimensione 4.66 MB
Formato Adobe PDF
4.66 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/377765
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact