Evaluating Morphing Attack Detectors: an Overview of the Bologna Online Evaluation Platform

The goal of the Bologna Online Evaluation Platform is to provide a trusted platform that can be used to evaluate the performance of face morphing attack detection (MAD) algorithms.​ The platform can be used by academic groups, industry actors, and independent developers to evaluate the performance of both Single Image and Differential attack detectors on various benchmarks. Tests are carried out on sequestered datasets in a controlled environment​. In this talk, the BOEP platform will be briefly described and the main steps needed to submit an algorithm for evaluation will be shown.

Curriculum vitæ

Lorenzo Pellegrini is a post-doc at the University of Bologna, where he received his Ph.D. with a dissertation on “Continual Learning for Computer Vision Applications”. He is mainly active in the Computer Vision area, with his main fields of interest being Continual Lifelong Learning and Biometric Systems. He has been focusing on privacy-sensitive Continual and Federated Learning, with a focus on biometric data. He collaborated in upgrading the FVC platform to allow modern Face Morphing algorithms to be tested on sequestered datasets in a controlled environment.

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