Context-Aware Restoration of Noisy Fingerprints
| Autor: | Indu Joshi, Tushar Prakash, B. S. Jaiswal, Rohit Kumar, Antitza Dantcheva, Sumantra Dutta Roy, Prem Kumar Kalra |
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| Přispěvatelé: | Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Delhi Technological University [New Delhi], Indian Institute of Technology Delhi (IIT Delhi) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Sensors Letters IEEE Sensors Letters, 2022, 6 (10), pp.6003704. ⟨10.1109/LSENS.2022.3203787⟩ |
| ISSN: | 2475-1472 |
| DOI: | 10.1109/LSENS.2022.3203787⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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