Towards quantifying the entropy of fingervein patterns across different feature extractors

Abstract

This paper makes a first attempt at quantifying the entropy of fingervein patterns that have been extracted using three different state-of-the-art feature extractors, on two publicly-available fingervein databases. We show that the resulting entropy is dependent upon both the feature extractor and database, implying that a universal estimate of fingervein entropy would be misleading. We also discuss how our entropy results can be applied towards more meaningful evaluations of the security and privacy of fingervein template protection schemes. Our open-source implementation of the entropy estimation on a publicly-available fingervein recognition system will help the research community to both validate our findings and build upon our work.

Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier

“…Since deoxyhemoglobin absorbs near infrared light in the blood, venous patterns appear as a series of dark lines. The nearby infrared light in combination with a special camera captures an image of the pattern of the finger veins [10].…”

Towards Measuring the Amount of Discriminatory Information in Finger Vein Biometric Characteristics Using a Relative Entropy Estimator

“…Finger vein patterns were extracted and compared using the bob.bio.vein PyPI package. 5 To extract the vein patterns from the finger images in each database, the fingers were first cropped and horizontally aligned as per [13,14]. Next, the finger vein pattern was extracted from the cropped finger images using three well-known feature extractors: Wide Line Detector (WLD) [14], Repeated Line Tracking (RLT) [15] and Maximum Curvature (MC) [16].…”

“…The number of degrees of freedom of the representative binomial distribution approximates the number of independent bits in each binary IrisCode, which in turn provides an estimate for the discrimination entropy of the underlying biometric characteristic. This approach was adopted to measure the entropy of finger vein patterns in [5]. However, as explained in [5], while this method of measuring entropy is correct from the source coding point of view, the issue with calculating the entropy in this way is that it only provides a reasonable estimate of the amount of biometric information if there is no variation between multiple samples captured from the same biometric instance.…”

“…This approach was adopted to measure the entropy of finger vein patterns in [5]. However, as explained in [5], while this method of measuring entropy is correct from the source coding point of view, the issue with calculating the entropy in this way is that it only provides a reasonable estimate of the amount of biometric information if there is no variation between multiple samples captured from the same biometric instance. Since this intra-class variation is unlikely to be zero in practice, the discrimination entropy would probably overestimate the amount of available biometric information [6,7].…”

“…https://www.idiap.ch/dataset/vera-fingervein 4. http://scs.ewi.utwente.nl/downloads/show,Finger%20Vein/ 5. https://pypi.python.org/pypi/bob.bio.vein.…”