Vascular Biometric Graph Comparison: Theory and Performance

Abstract

Vascular biometric templates are gaining increasing popularity due to simple and contact free capture and resilience to presentation attacks. We present the state of the art in Biometric Graph Comparison, a technique to register and compare vascular biometric templates by representing them as formal graphs. Such graphs consist of a set of vertices, representing the branch, termination and crossover points in the vascular pattern, and a set of edges. An edge represents the relationship between a pair of feature points that are directly connected by a vessel segment in a vascular biometric image. We summarise how this information has been successfully used over the past 8 years to improve registration and recognition performance for the vasculature under the palm, wrist, hand and retina. The structural properties of biometric graphs from these modalities differ, with retina graphs having the largest number of vertices on average and the most complex structure, and hand graphs having the smallest number of vertices on average and being the least connected. All vascular graphs have similarities to trees, with the ratio of edges to vertices being close to 1. We describe our most recent algorithms for biometric graph registration and comparison, and our performance results. We are interested in the possibility of using biometric graphs in a template protection scheme based on the paradigm of dissimilarity vectors. As a first step, we wish to improve registration. Certain modalities like retina have an intrinsic reference frame that makes registration more straightforward. Other modalities may not have an intrinsic reference frame. To overcome this, we introduce the notion of anchors—subgraphs of a biometric graph, having between 5 and 10 vertices, that occur consistently in samples from the same individual—that would enable the dissimilarity vector scheme to be applied to any vascular modality. Experiments on palm and wrist databases show that all individuals had at least some sets of 6 captures which could be used to identify an anchor, and anchors were identified in $$94\%$$ and $$88\%$$ for the palm and wrist databases, respectively.