Inverse Biometrics: Reconstructing Grayscale Finger Vein Images from Binary Features

Abstract

In this work, we investigate the possibility of generating a grayscale image of the finger vein from its binary template. This exercise would allow us to determine the invertibility of finger vein templates, and this has implications in biometric security and privacy. While such an analysis has been undertaken in the context of face, fingerprint and iris templates, this is the first work involving the finger vein biometric trait. The transformation from binary features to a grayscale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 7 distinct finger vein datasets. Results indicate that (a) it is possible to reconstruct finger vein images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization).