Search Results for author: Luiz A. Zanlorensi

Found 12 papers, 2 papers with code

Leveraging Model Fusion for Improved License Plate Recognition

no code implementations8 Sep 2023 Rayson Laroca, Luiz A. Zanlorensi, Valter Estevam, Rodrigo Minetto, David Menotti

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

License Plate Recognition Management

A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios

no code implementations24 Nov 2020 Luiz A. Zanlorensi, Rayson Laroca, Diego R. Lucio, Lucas R. Santos, Alceu S. Britto Jr., David Menotti

Thus, the use of datasets containing many subjects is essential to assess biometric systems' capacity to extract discriminating information from the periocular region.

Face Recognition Image Classification +1

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

no code implementations10 Feb 2020 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 quality of the obtained data.

Attribute

Deep Representations for Cross-spectral Ocular Biometrics

no code implementations21 Nov 2019 Luiz A. Zanlorensi, Diego R. Lucio, Alceu S. Britto Jr., Hugo Proença, David Menotti

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-infrared (NIR)).

Face Recognition

Ocular Recognition Databases and Competitions: A Survey

no code implementations21 Nov 2019 Luiz A. Zanlorensi, Rayson Laroca, Eduardo Luz, Alceu S. Britto Jr., Luiz S. Oliveira, David Menotti

The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information.

An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector

1 code implementation4 Sep 2019 Rayson Laroca, Luiz A. Zanlorensi, Gabriel R. Gonçalves, Eduardo Todt, William Robson Schwartz, David Menotti

This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules.

Data Augmentation License Plate Detection +2

Simultaneous Iris and Periocular Region Detection Using Coarse Annotations

no code implementations31 Jul 2019 Diego R. Lucio, Rayson Laroca, Luiz A. Zanlorensi, Gladston Moreira, David Menotti

In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN.

Iris Segmentation

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

2 code implementations26 Feb 2018 Rayson Laroca, Evair Severo, Luiz A. Zanlorensi, Luiz S. Oliveira, Gabriel Resende Gonçalves, William Robson Schwartz, David Menotti

First, in the SSIG dataset, composed of 2, 000 frames from 101 vehicle videos, our system achieved a recognition rate of 93. 53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89. 80% and 93. 03%, respectively) and considerably outperforming previous results (81. 80%).

Data Augmentation License Plate Detection +2

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