22 citations · 23 across the 3 of their papers we have counts for
7 papers
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…
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…
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…
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
Rayson Laroca, Luiz A. Zanlorensi, Gabriel R. Gonçalves +3
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…
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments
Luiz A. Zanlorensi, Eduardo Luz, Rayson Laroca +3
The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognitio…
A Benchmark for Iris Location and a Deep Learning Detector Evaluation
Evair Severo, Rayson Laroca, Cides S. Bezerra +4
The iris is considered as the biometric trait with the highest unique probability. The iris location is an important task for biometrics systems, affecting directly the results obt…