Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez, Aythami Morales, Ignacio Serna. OTB-morph: One-time Biometrics via Morphing. Machine Intelligence Research.
Citation: Mahdi Ghafourian, Julian Fierrez, Ruben Vera-Rodriguez, Aythami Morales, Ignacio Serna. OTB-morph: One-time Biometrics via Morphing. Machine Intelligence Research.

OTB-morph: One-time Biometrics via Morphing

doi: 10.1007/s11633-023-1432-x
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  • Author Bio:

    Mahdi Ghafourian received the B. Sc. degree in computer science from the Islamic Azad University of Mashhad, Iran in 2011, and the M. Sc. degree in information security and assurance from the Imam Reza University, Iran in 2016. He achieved the second position among top Master′s degree graduates. In 2021, he started the Ph.D. with Marie Curie scholarship within the EU ITN project PriMa (Privacy Matters) in the Biometrics and Data Pattern Analytics Laboratory – BiDA-Lab, at the Universidad Autonoma de Madrid, Spain.His research interests include information security, biometrics protection, face recognition, adversarial examples and federated learning. E-mail: (Corresponding author) ORCID iD: 0000-0003-4206-4873

    Julian Fierrez received the M. Sc. and the Ph. D. degrees in telecommunications engineering from Universidad Politecnica de Madrid, Spain in 2001 and 2006, respectively. Since 2004, he is at Universidad Autonoma de Madrid, Spain where he is associate professor since 2010. He is Associate Editor for Information Fusion, IEEE Transactions on Information Forensics and Security, and IEEE Transactions on Image Processing. He has received best papers awards at AVBPA, ICB, IJCB, ICPR, ICPRS, and Pattern Recognition Letters; and several research distinctions, including: EBF European Biometric Industry Award 2006, EURASIP Best Ph. D. Award 2012, Miguel Catalan Award to the Best Researcher under 40 in the Community of Madrid in the general area of Science and Technology, and the IAPR Young Biometrics Investigator Award 2017. Since 2020, he is member of the ELLIS Society. His research interests include signal and image processing, AI fundamentals and applications, HCI, forensics, and biometrics for security and human behavior analysis. E-mail:

    Ruben Vera-Rodriguez received the M. Sc. degree in telecommunications engineering from Universidad de Sevilla Spain, Spain in 2006, and the Ph. D. degree in electrical and electronic engineering from Swansea University, UK in 2010. Since 2010, he has been affiliated with the Biometric Recognition Group, Universidad Autonoma de Madrid, Spain, where he is currently an associate professor since 2018. Ruben has published over 100 scientific articles published in international journals and conferences. He is actively involved in several National and European projects focused on biometrics. He has been Program Chair for the 51st IEEE International Carnahan Conference on Security and Technology (ICCST) in 2017; the 23rd Iberoamerican Congress on Pattern Recognition (CIARP 2018) in 2018; and the International Conference on Biometric Engineering and Applications (ICBEA 2019) in 2019. His research interests include signal and image processing, pattern recognition, HCI, and biometrics, with emphasis on signature, face, gait verification and forensic applications of biometrics. E-mail:

    Aythami Morales received the M. Sc. degree in electrical engineering from Universidad de Las Palmas de Gran Canaria, Spain in 2006, the Ph. D. degree in artificial intelligence from La Universidad de Las Palmas de Gran Canaria, Spain in 2011. Since 2017, he is associate professor with the Universidad Autonoma de Madrid, Spain. His interests include machine learning, biometric processing, security and privacy. E-mail:

    Ignacio Serna received the B. Sc. degree in mathematics and the B. Sc. degree in computer science from the Autonomous University of Madrid, Spain in 2018, and the M. Sc. degree in artificial intelligence from the Autonomous University of Madrid, Spain in 2020. He is currently a Ph. D. degree candidate in computer science at the BiDA-Lab, Spain. His research interests include computer vision, pattern recognition and explainable AI, with applications to biometric. E-mail:

  • Received Date: 2022-09-27
  • Accepted Date: 2023-03-02
  • Publish Online: 2023-06-01
  • Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.


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    22 This kind of impostors are different from the attackers considered in Section 3, who have much more information to attack the system compared to a random impostor that just tries to illegally access the system by using his own face input and no other methods to improve the attack success.
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