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.
Highlights
A BIOMETRIC system may be viewed as a pattern recognition system consisting of a sensor module, a feature extractor module and a comparison module [27]
We addressed and demonstrated the following issues: (a) a suitable Convolutional Neural Network (CNN) architecture to reconstruct gray-scale images from feature-rich binary images; (b) reconstructing gray-scale finger vein images from binary templates pertaining to a single given dataset; and (c) reconstructing gray-scale finger vein images from binary templates in a dataset that was not used for training the CNN
An inverse biometric threat assessment as proposed by [19] (IAMR) which is a recent methodology to evaluate inverse biometric approaches. These templates can be input to the trained CNN model to regenerate digital images which can be used to extract additional information about individuals or to attack the biometric system based on finger/hand vascular patterns
Summary
A BIOMETRIC system may be viewed as a pattern recognition system consisting of a sensor module, a feature extractor module and a comparison module [27]. KAUBA et al.: INVERSE BIOMETRICS: GENERATING VASCULAR IMAGES FROM BINARY TEMPLATES an inverse biometric threat assessment as proposed by [19] (IAMR) which is a recent methodology to evaluate inverse biometric approaches These templates can be input to the trained CNN model to regenerate digital images which can be used to extract additional information about individuals or to attack the biometric system based on finger/hand vascular patterns. This research is based on our previous work [30], where we showed that it is possible to reconstruct gray-scale finger vein images from their binary feature templates.
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