Outputs (3)

Designing a facial spoofing database for processed image attacks (2016)
Presentation / Conference Contribution
Omar, L., & Ivrissimtzis, I. (2016, November). Designing a facial spoofing database for processed image attacks. Presented at 7th International Conference on Imaging for Crime Detection and Prevention., Madrid, Spain

Face recognition systems are used for user authentication in everyday applications such as logging into a laptop or smartphone without need to memorize a password. However, they are still vulnerable to spoofing attacks, as for example when an imposte... Read More about Designing a facial spoofing database for processed image attacks.

Resilience of Luminance based Liveness Tests under Attacks with Processed Imposter Images (2016)
Presentation / Conference Contribution
Omar, L., & Ivrissimtzis, I. (2023, May). Resilience of Luminance based Liveness Tests under Attacks with Processed Imposter Images. Presented at 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 (WSCG 2016)., Plzen, Czech Republic

Liveness tests are techniques employed by face recognition authentication systems, aiming at verifying that a live face rather than a photo is standing in front of the system camera. In this paper, we study the resilience of a standard liveness test... Read More about Resilience of Luminance based Liveness Tests under Attacks with Processed Imposter Images.

Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks (2015)
Presentation / Conference Contribution
Omar, L., & Ivrissimtzis, I. (2015, September). Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks. Presented at 7th UK Computer Vision Student Workshop (BMVW), Swansea, UK

This paper presents an experiment designed to test the resilience of several user verification systems based on face recognition technology against simple identity spoofing methods, such as trying to gain access to the system by using mobile camera s... Read More about Evaluating the Resilience of Face Recognition Systems Against Malicious Attacks.