Face template protection via random permutation maxout transform

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


Face template protection is of great interest nowadays due to the increasing concerns on privacy and security of the face templates stored in the databases. There were many attempts to develop plausible face template protection schemes that can satisfy four design criteria of biometric template protection, namely noninvertibility, cancellability, non-linkability and performance. In this paper, a cancellable face template scheme, namely random permutation maxout (RPM) transform is proposed. The RPM transforms a real-valued face feature vector (template) into a discrete index code as a means of protected form of face template. Such a transform offers two major merits: 1) robustness to noises in numeric values of original face template; and 2) nonlinear embedding based on the implicit order of the data. The former promotes accuracy performance preservation while the latter offers strong non-invertible transformation that leads to hardness in inversion attack. Several experiments based on the AR face database are conducted to observe the RPM transform performance with respect to its various parameters. The analyses justify its resilience to inversion attack as well as satisfy the revocability and non-linkability criteria of cancellable biometrics.

Original languageEnglish
Title of host publicationProceedings of 2017 International Conference on Biometrics Engineering and Application, ICBEA 2017
PublisherAssociation for Computing Machinery
Number of pages7
ISBN (Electronic)9781450348713
Publication statusPublished - 2017 Apr 21
Event2017 International Conference on Biometrics Engineering and Application, ICBEA 2017 - Hong Kong, Hong Kong
Duration: 2017 Apr 212017 Apr 23

Publication series

NameACM International Conference Proceeding Series
VolumePart F128052


Other2017 International Conference on Biometrics Engineering and Application, ICBEA 2017
Country/TerritoryHong Kong
CityHong Kong

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIP) (NO. 2016R1A2B4011656)

Publisher Copyright:
© 2017 Association for Computing Machinery.

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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