English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Face recognition systems are widely deployed in high-security applicationssuch as for biometric verification at border controls. Despite their highaccuracy on pristine data, it is well-known that digital manipulations, such asface morphing, pose a security threat to face recognition systems. Maliciousactors can exploit the facilities offered by the identity document issuanceprocess to obtain identity documents containing morphed images. Thus, subjectswho contributed to the creation of the morphed image can with high probabilityuse the identity document to bypass automated face recognition systems. Inrecent years, no-reference (i.e., single image) and differential morphingattack detectors have been proposed to tackle this risk. These systems aretypically evaluated in isolation from the face recognition system that theyhave to operate jointly with and do not consider the face recognition process.Contrary to most existing works, we present a novel method for adapting deeplearning-based face recognition systems to be more robust against face morphingattacks. To this end, we introduce TetraLoss, a novel loss function that learnsto separate morphed face images from its contributing subjects in the embeddingspace while still preserving high biometric verification performance. In acomprehensive evaluation, we show that the proposed method can significantlyenhance the original system while also significantly outperforming other testedbaseline methods.

TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks