Protection of Sparse Retinal Templates Using Cohort-Based Dissimilarity Vectors

Abstract

Retinal vasculature is a biometric characteristic that is highly accurate for recognition but for which no template protection scheme exists. We propose the first retinal template protection scheme, adapting an existing paradigm of cohort-based modelling to templates containing the node and edge data of retinal graphs. The template protection scheme results in at most 2.3% reduction in accuracy compared to unprotected templates. A common concern with cohort based systems is that the availability of distance scores can be exploited to reconstruct the biometric image or biometric template using inversion attack. On the contrary, we show that using our sparse templates in a cohort-based system results in less than 0.3% success rate for an inverse biometric attack. In addition, rigorous unlinkability analysis shows that the template protection scheme has linkability scores at least as low as or lower than the state-of-the-art template protection schemes.