Inverse Biometrics: Generating Vascular Images from Binary Templates

Abstract

In this work, we investigate the possibility of generating grayscale images of finger and hand vein patterns from their corresponding binary templates. This would allow us to determine the invertibility of vascular templates, which has implications in biometric security and privacy. The transformation from binary features to a gray-scale 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 8 distinct finger vein datasets and 3 hand vein datasets. Results indicate that (a) it is possible to reconstruct the considered vascular 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). The results of this research further underscore the need for properly securing biometric templates, even if they are of binary nature.