Evaluating the Integration of Morph Attack Detection in Automated Face Recognition Systems
Andrea Panzino, Simone Maurizio La Cava, Giulia Orrù, Gian Luca Marcialis · 2024
Due to the possibility of automatically verifying an individual’s identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.