Arathi Arakala

Minutiae-Based Structures for A Fuzzy Vault

One vital application of biometrics is to supplement or replace passwords to provide secure authentication. Cryptographic schemes using passwords require exactly the same password at enrolment and verification to authenticate successfully. The inherent variation in samples of the same biometric makes it difficult to replace passwords directly with biometrics in a cryptographic scheme. The fuzzy vault is an innovative cryptographic construct that uses error correction techniques to compensate for biometric variation. Our research is directed to methods of realizing the fuzzy vault for the fingerprint biometric using minutia points described in a translation and rotation invariant manner. We investigate three different minutia representation methods, which are translation and rotation invariant. We study their robustness and determine their suitability to be incorporated in a fuzzy vault construct. We finally show that one of our three chosen structures shows promise for incorporation into a fuzzy vault scheme.

Retina Verification System Based on Biometric Graph Matching

This paper presents an automatic retina verification framework based on the biometric graph matching (BGM) algorithm. The retinal vasculature is extracted using a family of matched filters in the frequency domain and morphological operators. Then, retinal templates are defined as formal spatial graphs derived from the retinal vasculature. The BGM algorithm, a noisy graph matching algorithm, robust to translation, non-linear distortion, and small rotations, is used to compare retinal templates. The BGM algorithm uses graph topology to define three distance measures between a pair of graphs, two of which are new. A support vector machine (SVM) classifier is used to distinguish between genuine and imposter comparisons. Using single as well as multiple graph measures, the classifier achieves complete separation on a training set of images from the VARIA database (60% of the data), equaling the state-of-the-art for retina verification. Because the available data set is small, kernel density estimation (KDE) of the genuine and imposter score distributions of the training set are used to measure performance of the BGM algorithm. In the one dimensional case, the KDE model is validated with the testing set. A 0 EER on testing shows that the KDE model is a good fit for the empirical distribution. For the multiple graph measures, a novel combination of the SVM boundary and the KDE model is used to obtain a fair comparison with the KDE model for the single measure. A clear benefit in using multiple graph measures over a single measure to distinguish genuine and imposter comparisons is demonstrated by a drop in theoretical error of between 60% and more than two orders of magnitude.

Fingerprint Alignment for A Minutiae-Based Fuzzy Vault

The fuzzy vault is an innovative cryptographic construct that uses error correction techniques to compensate for natural biometric variation. For fingerprints, the fuzzy vault can be used to compensate for the insertion and deletion of minutiae between samples, within the cryptographic framework. However, fingerprint biometrics also suffer from the problem that samples at enrolment and verification cannot be captured and recorded within a universally agreed frame of reference. There is currently no efficient fingerprint pre-alignment technique that also protects the template. In this paper we propose a pre-alignment algorithm that incorporates quantifiable template protection and explore the suitability of three minutiae-based structures for the algorithm. We find that one of the structures is strongly suitable with respect to the goals of our pre-alignment algorithm and its impact on the false non-match rate of an overall system is quantified. Our research also clarifies the key characteristics required from minutiae-based structures for high performance.

Hand vein authentication using biometric graph matching

This study proposes an automatic dorsal hand vein verification system using a novel algorithm called biometric graph matching (BGM). The dorsal hand vein image is segmented using the K-means technique and the region of interest is extracted based on the morphological analysis operators and normalised using adaptive histogram equalisation. Veins are extracted using a maximum curvature algorithm. The locations and vascular connections between crossovers, bifurcations and terminations in a hand vein pattern define a hand vein graph. The matching performance of BGM for hand vein graphs is tested with two cost functions and compared with the matching performance of two standard point patterns matching algorithms, iterative closest point (ICP) and modified Hausdorff distance. Experiments are conducted on two public databases captured using far infrared and near infrared (NIR) cameras. BGMs matching performance is competitive with state-of-the-art algorithms on the databases despite using small and concise templates. For both databases, BGM performed at least as well as ICP. For the small sized graphs from the NIR database, BGM significantly outperformed point pattern matching. The size of the common subgraph of a pair of graphs is the most significant discriminating measure between genuine and imposter comparisons.

Retina features based on vessel graph substructures

We represent the retina vessel pattern as a spatial relational graph, and match features using error-correcting graph matching. We study the distinctiveness of the nodes (branching and crossing points) compared with that of the edges and other substructures (nodes of degree k, paths of length k). On a training set from the VARIA database, we show that as well as nodes, three other types of graph sub-structure completely or almost completely separate genuine from imposter comparisons. We show that combining nodes and edges can improve the separation distance. We identify two retina graph statistics, the edge-to-node ratio and the variance of the degree distribution, that have low correlation with node match score.

Fingerprints as Spatial Graphs: Nodes and Edges

Point-pattern matching of minutiae is the most common method used in fingerprint biometrics, but it is generally insufficient by itself. It has particular limitations in matching partial prints or in secure (biocryptographic) matching. Here, we add structure with a new spatial graph represention of a fingerprint, with minutiae as nodes. Using a sample of fingerprint graphs extracted from the FVC2002 database, we show that matching fingerprints using only the edges of the graphs performs almost as well as using only the nodes. Combinations of edges and nodes have superior performance to either individual score.

Fractional biometrics: safeguarding privacy in biometric applications

This paper presents a biometric system solution that “masks” a fraction of a person’s biometric image before submission, to reduce the possibility of forgery and collusion. A prototype system was constructed for the fingerprint biometric and tested in three security scenarios. It is shown that implementing the fractional biometric system does not significantly affect accuracy. We provide theoretical security analysis on the guessing entropy of a Fractional Template and the security against collusion. We demonstrate that by masking above 50% of the biometric features, we achieve a sufficient mix of security, robustness and accuracy to warrant further study. When 75% of the features are masked, we found that the theoretical guessing entropy is 42 bits, and we found that, on average, 5 authenticators had to collude before the system would be compromised.

Entropy of the Retina Template

We compare two vessel extraction methods for creation of a retina template, using a database of 20 images of normal retinas. Each vessel in a well defined region is represented by a three dimensional feature, from which a retina template is built. Based on the sample distributions, we propose a preliminary theoretical model to predict the entropy of a retina template. We analyse by experimental and theoretical means the entropy present, and infer that entropy from our retina template compares sufficiently favourably with that of a minutia-based fingerprint template to warrant further study.

Ballprints as an infant biometric: A first approach

There is an urgent need for a biometric that can be used for reliable identification of very young children (0 - 4 years of age), to fight child trafficking and improve vaccination uptake in the developing world. It remains unclear if the most common adult biometrics, such as face, fingerprint and iris, are stable during the first few years of life.

Entropy of Feature Point-Based Retina Templates

This paper studies the amount of distinctive information contained in a privacy protecting and compact template of a retinal image created from the locations of crossings and bifurcations in the choroidal vasculature, otherwise called feature points. Using a training set of 20 different retina, we build a template generator that simulates one million imposter comparisons and computes the number of imposter retina comparisons that successfully matched at various thresholds. The template entropy thus computed was used to validate a theoretical model of imposter comparisons. The simulator and the model both estimate that 20 bits of entropy can be achieved by the feature point-based template. Our results reveal the distinctiveness of feature point-based retinal templates, hence establishing their potential as a biometric identifier for high security and memory intensive applications.