mlsanityData to 7 Oct 2026

OTB-morph: One-Time Biometrics via Morphing applied to Face Templates

arXiv 2111.13213 ↗First posted 25 Nov 2021cs.CV cs.CR cs.LGcited by 4

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Abstract (arXiv)

Cancelable biometrics refers to a group of techniques in which the biometric inputs are transformed intentionally using a key before processing or storage. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new scheme for cancelable biometrics aimed at protecting the templates against potential attacks, applicable to any biometric-based recognition system. Our proposed scheme is based on time-varying keys obtained from morphing random biometric information. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand against leakage attacks while improving the recognition performance.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 52 references that are in the map. First posted on arXiv 25 Nov 2021.

Builds on

9 of 52 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019Deep Leakage from Gradientscited by 1,004
  3. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  4. 2014Learning Face Representation from Scratchcited by 451
  5. 2020Face Morphing Attack Generation & Detection: A Comprehensive Surveycited by 63
  6. 2019GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detectioncited by 42
  7. 2020DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representationscited by 26
  8. 2019Biometric Template Storage with Blockchain: A First Look into Cost and Performance Tradeoffscited by 6
  9. 2020Are Adaptive Face Recognition Systems still Necessary? Experiments on the APE Datasetcited by 1

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Cited by year

Citing papers per year: 2023: 4.

4 papers.