Deep Learning-Based Fingerprint Quality Assessment

We investigate the capabilities of deep learning methods for the quality assessment of fingerprint samples. Starting from general considerations for the implementation of a deep learning-based fingerprint quality assessment, we modify and fine-tune six convolutional neural networks and one vision transformer for the specific task. For this, we generate a synthetic training database and propose a labelling based on the normalized average sample comparison score. The obtained results in terms of error vs. discard characteristic curves show that all deep learning methods outperform the NFIQ 2.3 baseline. Most notably, the best-performing VGG16 model results in a superior predictive performance than NFIQ 2.3 on all tested datasets. A detailed interpretation of the obtained results paves the way for more specific and tailored solutions. We suggest to consider the proposed algorithms as a starting point for a development of a deep learning-based fingerprint quality assessment methods and make them publicly available.