Gender Obfuscation through Face Morphing

Shunxin Wang, Una M. Kelly, Raymond N.J. Veldhuis

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
63 Downloads (Pure)


With the advancement of machine learning, facial biometric data has been widely adopted for person recognition. Soft biometrics such as gender, age and ethnicity can be extracted automatically from the facial photographs without permission. This raises privacy concerns since such auxiliary information might be utilized improperly. In this work, we apply face morphing to obfuscate gender information in face images, so that gender classifiers can no longer accurately predict gender, but the resulting images can still be used for identity verification. We further explore the reversibility of our approach and the results show that gender obfuscated through face morphing cannot be recovered or retrieved easily. Our approach is especially useful for an identity verification system which is sensitive to morphing attacks.

Original languageEnglish
Title of host publicationProceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021
Place of PublicationPiscataway, NJ
ISBN (Electronic)978-1-7281-9556-8
ISBN (Print)978-1-7281-9557-5
Publication statusPublished - 29 Jun 2021
Event9th International Workshop on Biometrics and Forensics, IWBF 2021 - Rome, Italy
Duration: 6 May 20217 May 2021


Conference9th International Workshop on Biometrics and Forensics, IWBF 2021


  • Facial attribute manipulation
  • Gender obfuscation
  • Image forensics


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