A framework for multimodal biometric authentication systems with template protection

Zheng Hui Goh, Yandan Wang, Lu Leng, Shiuan-Ni Liang, Zhe Jin, Yen-Lung Lai, Xin Wang

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

A multimodal biometric authentication framework based on Index-of-Max (IoM) hashing, Alignment-Free Hashing (AFH), and feature-level fusion is proposed in this paper. This framework enjoys three major merits: 1) Biometric templates are secured by biometric template protection technology (i.e., IoM hashing), thus providing strong resistance to security and privacy invasion; 2) It flexibly adopts a variety of biometric feature representations (e.g., binary, and real-valued), thus generalizing to a wide range of biometric features for fusion; 3) Feature-level fusion, which has low template storage, low matching computational complexity, and low privacy risks, can be accomplished without alignment via AFH. Specifically, the proposed framework works as a drag-and-drop mode that can quickly adopt all popular biometric modalities with different feature distributions for feature-level fusion. The fused templates are produced using operators AND, OR and XOR in binary domain. To evaluate the proposed framework, benchmarking datasets from four widely deployed biometric modalities (i.e., fingerprint for FVC 2002, face for LFW, iris for CASIA-v3-Interval, and finger-vein for UTFVP) are used. The experimental results presented in Table 5 suggest that the proposed framework can achieve state-of-the-art performance in most of the datasets while offering additional folds, such as template protection and generalization to variable features. Moreover, biometric template protection criteria (irreversibility, unlinkability, and revocability) are also analyzed. The results of the analysis indicate satisfaction in terms of the security and privacy of the templates generated from the proposed framework.

Original languageEnglish
Pages (from-to)96388-96402
Number of pages15
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 8 Sept 2022

Keywords

  • Biometrics (access control)
  • Face recognition
  • Feature extraction
  • Feature-level fusion
  • Fingerprint recognition
  • Iris recognition
  • multimodal biometrics
  • Privacy
  • privacy and security
  • Security

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