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20182025
most citedDeep Representations for Cross-spectral Ocular Biometrics

22 citations · 23 across the 5 of their papers we have counts for

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cs.CV2025

An on-production high-resolution longitudinal neonatal fingerprint database in Brazil

Luiz F. P. Southier, Marcelo Filipak, Luiz A. Zanlorensi +5

The neonatal period is critical for survival, requiring accurate and early identification to enable timely interventions such as vaccinations, HIV treatment, and nutrition programs…

cs.CV2024

Using Deep Neural Networks to Quantify Parking Dwell Time

Marcelo Eduardo Marques Ribas, Heloisa Benedet Mendes, Luiz Eduardo Soares de Oliveira +2

In smart cities, it is common practice to define a maximum length of stay for a given parking space to increase the space's rotativity and discourage the usage of individual transp…

cs.CV2023

Leveraging Model Fusion for Improved License Plate Recognition

Rayson Laroca, Luiz A. Zanlorensi, Valter Estevam +2

License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement. Although LPR has witnessed…

cs.CV20221 cited

ORCNet: A context-based network to simultaneously segment the ocular region components

Diego Rafael Lucio, Luiz A. Zanlorensi, Yandre Maldonado e Gomes da Costa +1

Accurate extraction of the Region of Interest is critical for successful ocular region-based biometrics. In this direction, we propose a new context-based segmentation approach, en…

cs.CV2020

Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization

Luiz A. Zanlorensi, Hugo Proença, David Menotti

Ocular biometric systems working in unconstrained environments usually face the problem of small within-class compactness caused by the multiple factors that jointly degrade the qu…

cs.CV201922 cited

Deep Representations for Cross-spectral Ocular Biometrics

Luiz A. Zanlorensi, Diego R. Lucio, Alceu S. Britto +2

One of the major challenges in ocular biometrics is the cross-spectral scenario, i.e., how to match images acquired in different wavelengths (typically visible (VIS) against near-i…