Context-Aware Restoration of Noisy Fingerprints

Abstract

The literature on fingerprint restoration algorithms firmly advocates exploiting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contextual information,</i> such as ridge orientation field, ridge spacing, and ridge frequency, to recover ridge details in fingerprint regions with poor quality ridge structure. However, most state-of-the-art convolutional neural network based fingerprint restoration models exploit spatial context only through convolution operations. Motivated by this observation, this article introduces a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">context-aware</i> fingerprint restoration model: context-aware GAN (CA-GAN). CA-GAN is explicitly regularized to learn spatial context by ensuring that the model not only performs fingerprint restoration but also accurately predicts the correct spatial arrangement of randomly arranged fingerprint patches. Experimental results establish better fingerprint restoration ability of CA-GAN compared to the state-of-the-art.