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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.
Highlights
The purpose of this Chapter is to provide a single resource for biometric researchers to learn and use the current state of the art in Biometric Graph Comparison1 for vascular modalities.Vascular biometric recognition is the process of identifying and verifying an individual using the intricate vascular pattern in the body
We summarise experimental results we have obtained by applying Biometric Graph Comparison (BGC) to Biometric Graph (BG) from databases of the four modalities we have studied
The BGC algorithm has been tested on five vascular modalities: Palm vessels representing the vascular pattern under the palm of the hand; Wrist vessels representing the vascular pattern on the inside of the wrists; Hand vessels representing the vascular pattern under the skin on the back of the hand; Retina vessels representing the vascular pattern supplying blood to the retina; and Finger vessels representing the vascular pattern under the skin of the finger
Summary
The purpose of this Chapter is to provide a single resource for biometric researchers to learn and use the current state of the art in Biometric Graph Comparison for vascular modalities. Biometric Graph Comparison (BGC) is a feature-based process, which enhances and improves on traditional point pattern matching methods for many vascular modalities. Its key idea is the replacement of a feature point based representation of a biometric image by a spatial graph based representation, where the graph edges provide a formal and concise representation of the vessel segments between feature points, incorporating connectivity of feature points into the biometric template. This added dimension makes the concepts and techniques of graph theory newly available to vascular biometric identification and verification. We present our first results on a potential solution to this problem, where we look for small but characteristic structures we call “anchors”, which appear in sufficiently many of an individual’s samples to be used for registration
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