Alignment-robust cancelable biometric scheme for iris verification

Ming Jie Lee, Zhe Jin, Shiuan-Ni Liang, Massimo Tistarelli

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)


In this paper, we propose a histogram of oriented gradient inspired cancelable biometrics-Random Augmented Histogram of Gradients (R HoG) for iris template protection. The proposed R HoG is built upon on two main components: 1) column vector random augmentation and 2) gradient orientation grouping mechanisms to transform the unaligned irisCode feature into the alignment-robust cancelable template. The alignment-robust property of the proposed R HoG enables the fast template comparison which is crucial for an efficient authentication process. Experiments were performed on CASIA-IrisV3-Internal and CASIA-IrisV4-Thousand datasets. The results demonstrate the proposed R HoG could achieve acceptable verification performance in both datasets. Other than that, the irreversibility and security properties are studied based on major security and privacy attacks in biometric system. Lastly, results from the benchmarking evaluation framework show the proposed method is satisfying the unlinkability property.

Original languageEnglish
Pages (from-to)3449-3464
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Publication statusPublished - 2022


  • cancelable biometrics
  • histogram of oriented gradient
  • Iris
  • security and privacy

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