Genface: Improving cyber security using realistic synthetic face generation

Margarita Osadchy, Yan Wang, Orr Dunkelman, Stuart Gibson, Julio Hernandez-Castro, Christopher Solomon

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


Recent advances in face recognition technology render face-based authentication very attractive due to the high accuracy and ease of use. However, the increased use of biometrics (such as faces) triggered a lot of research on the protection biometric data in the fields of computer security and cryptography. Unfortunately, most of the face-based systems, and most notably the privacy-preserving mechanisms, are evaluated on small data sets or assume ideal distributions of the faces (that could differ significantly from the real data). At the same time, acquiring large biometric data sets for evaluation purposes is time consuming, expensive, and complicated due to legal/ethical considerations related to the privacy of the test subjects. In this work, we present GenFace, the first publicly available system for generating synthetic facial images. GenFace can generate sets of large number of facial images, solving the aforementioned problem. Such sets can be used for testing and evaluating face-based authentication systems. Such test sets can also be used in balancing the ROC curves of such systems with the error correction codes used in authentication systems employing secure sketch or fuzzy extractors. Another application is the use of these test sets in the evaluation of privacy-preserving biometric protocols such as GSHADE, which can now enjoy a large number of synthetic examples which follow a real-life distribution of biometrics. As a case study, we show how to use GenFace in evaluating SecureFace, a face-based authentication system that offers end-to-end authentication and privacy.

Original languageEnglish
Title of host publicationCyber Security Cryptography and Machine Learning - 1st International Conference, CSCML 2017, Proceedings
EditorsShlomi Dolev, Sachin Lodha
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319600796
StatePublished - 2017
Event1st International Conference on Cyber Security Cryptography and Machine Learning, CSCML 2017 - Beer-Sheva, Israel
Duration: 29 Jun 201730 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10332 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Conference on Cyber Security Cryptography and Machine Learning, CSCML 2017

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.


  • Biometrics
  • Face verification
  • Face-based authentication
  • GenFace
  • SecureFace
  • Synthetic face generation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Genface: Improving cyber security using realistic synthetic face generation'. Together they form a unique fingerprint.

Cite this