SecureFace: Face Template Protection

Guangcan Mai, Kai Cao, Xiangyuan Lan, Pong Chi Yuen*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

46 Citations (Scopus)


It has been shown that face images can be reconstructed from their representations (templates). We propose a randomized CNN to generate protected face biometric templates given the input face image and a user-specific key. The use of user-specific keys introduces randomness to the secure template and hence strengthens the template security. To further enhance the security of the templates, instead of storing the key, we store a secure sketch that can be decoded to generate the key with genuine queries submitted to the system. We have evaluated the proposed protected template generation method using three benchmarking datasets for the face (FRGC v2.0, CFP, and IJB-A). The experimental results justify that the protected template generated by the proposed method are non-invertible and cancellable, while preserving the verification performance.

Original languageEnglish
Pages (from-to)262-277
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Early online date15 Jul 2020
Publication statusPublished - Jan 2021

Scopus Subject Areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

User-Defined Keywords

  • Biometric
  • deep templates
  • protected templates
  • randomized CNN
  • template protection
  • template security


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