On restoration of degraded fingerprints

Abstract

The state-of-the-art fingerprint matching systems achieve high accuracy on good quality fingerprints. However, degraded fingerprints obtained due to poor skin conditions of subjects or fingerprints obtained around a crime scene often have noisy background and poor ridge structure. Such degraded fingerprints pose problem for the existing fingerprint recognition systems. This paper presents a fingerprint restoration model for a poor quality fingerprint that reconstructs a binarized fingerprint image with an improved ridge structure. In particular, we demonstrate the effectiveness of channel refinement in fingerprint restoration. The state-of-the-art channel refinement mechanisms, such as Squeeze and Excitation (SE) block, in general, create SE- block introduce redundancy among channel weights and degrade the performance of fingerprint enhancement models. We present a lightweight attention mechanism that performs channel refinement by reducing redundancy among channel weights of the convolutional kernels. Restored fingerprints generated after introducing proposed channel refinement unit obtain improved quality scores on standard fingerprint quality assessment tool. Furthermore, restored fingerprints achieve improved fingerprint matching performance. We also illustrate that the idea of introducing a channel refinement unit is generalizable to different deep architectures. Additionally, to quantify the ridge preservation ability of the model, standard metrics: Dice score, Jaccard Similarity, SSIM and PSNR are computed with the ground truth and the output of the model (CR-GAN). An ablation study is conducted to individually quantify the improvement of generator and discriminator sub-networks of CR-GAN through channel refinement. Experiments on the publicly available IIITD- MOLF, Rural Indian Fingerprint Database and a private rural fingerprint database demonstrate the efficacy of the proposed attention mechanism.