Inverse Biometrics: Generating Vascular Images From Binary Templates

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 transformati...

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Bibliographic Details
Published in:IEEE transactions on biometrics, behavior, and identity science behavior, and identity science, 2021-10, Vol.3 (4), p.464-478
Main Authors: Kauba, Christof, Kirchgasser, Simon, Mirjalili, Vahid, Uhl, Andreas, Ross, Arun
Format: Article
Language:English
Subjects:
Artificial neural networks
Biometrics
Biometrics (access control)
CNN-based reconstruction
Convolutional neural networks
Datasets
Feature extraction
Feature recognition
finger vein recognition
Gray scale
hand vein recognition
Image reconstruction
Inverse biometrics
Object recognition
template protection
Veins
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Summary: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.
ISSN:2637-6407
2637-6407
DOI:10.1109/TBIOM.2021.3073666