Fingerprint Image Restoration Using the U-Net Deep Learning Model

This thesis investigated the use of convolutional neural networks (CNNs) for image restoration of fingerprint images, focusing on the reconstruction from raw sensor images to denoised fingerprint images, suitable for matching. Fingerprint images from sensors often introduce noise and artifacts that inhibit the matching process. The goal was to develop a CNN-based approach capable of removing artifacts such as moiré patters that degrade fingerprint quality, thereby restoring the fingerprint image. A U-Net architecture was used as a baseline model and extended with several architectural modifications such as Convolutional Block Attention Modules (CBAM), gated skip connections and dilated convolutions. In addition, the effects of different batch sizes and learning rates for the ADAM optimizer were evaluated. The models were trained with synthetic fingerprint data, including a dataset augmented with image transformations to increase the training dataset size. The results show that CNN-based restoration can surpass traditional ISP pipelines, especially when training and test domains are well aligned. Learning rate selection held significant importance, with 10⁻⁴ consistently yielding the lowest False Reject Rates (FRR). Data augmentation improved robustness and frequently reduced FRR relative to identical models trained on non-augmented data. However, generalization to different datasets remained limited, highlighting the need for a training dataset representative of diverse data. The most promising model architecture for restoration was U-Net, which is the simplest of all the proposed models. Since the training dataset was very limited in size, the risk of overfitting was high, and it seemed that the more complex models tended to overfit relatively quickly. The simple U-Net, in contrast, seemed to generalize the best, and had more consistent performance on the testing data than every other model. Overall, it seems that CNN-based restoration methods offers a promising approach to reconstruct fingerprints from raw sensor images, and that their role and performance could be increased with further development.