Context-Aware Restoration of Noisy Fingerprints - Université Côte d'Azur Accéder directement au contenu
Article Dans Une Revue IEEE Sensors Letters Année : 2022

Context-Aware Restoration of Noisy Fingerprints

Résumé

The literature on fingerprint restoration algorithms firmly advocates exploiting contextual information, 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 context-aware 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.
Fichier principal
Vignette du fichier
sensorsl22_context_230112_152517.pdf (4.06 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03966800 , version 1 (31-01-2023)

Identifiants

Citer

Indu Joshi, Tushar Prakash, B. Jaiswal, Rohit Kumar, Antitza Dantcheva, et al.. Context-Aware Restoration of Noisy Fingerprints. IEEE Sensors Letters, 2022, 6 (10), pp.6003704. ⟨10.1109/LSENS.2022.3203787⟩. ⟨hal-03966800⟩
17 Consultations
49 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More