Random Projection-Based Cancelable Iris Biometrics for Human Identification Using Deep Learning

Cancelable biometrics serves as an effective countermeasure against various template attacks launched by intruders, safeguarding the biometric system. This paper proposes a cancelable approach with a novel feature extraction technique for iris recognition, known as the hybrid architecture of the con...

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Veröffentlicht in:Arabian journal for science and engineering (2011) 2024-03, Vol.49 (3), p.3815-3828
Hauptverfasser: Rani, Rajneesh, Dhir, Renu, Sonkar, Kirti
Format: Artikel
Sprache:eng
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Zusammenfassung:Cancelable biometrics serves as an effective countermeasure against various template attacks launched by intruders, safeguarding the biometric system. This paper proposes a cancelable approach with a novel feature extraction technique for iris recognition, known as the hybrid architecture of the convolutional neural network (CNN) and GRU (gated recurrent unit). To provide cancelability to the system, the paper makes use of a random projection technique. The proposed method has the best outcome in terms of accurate identification. The method is validated on two Iris datasets IITD and MMU, which show promising results on the equal error rate (EER) and accuracy. The proposed model provides 0.02 and 0.045 EER for IITD and MMU, respectively, and accuracy 0.98 and 0.933%, for IITD and MMU Iris dataset, respectively, which is very high compared to other methodologies. The proposed hybrid architecture is being used for a cancelable biometric system for the first time based on literature review. The efficiency of the proposed method is high when validated on the datasets.
ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-023-08190-0