arXiv subjects

Diogo Nunes

Publications and source records attributed to Diogo Nunes.

5 recordsLinked to original sources

arXiv2026

DFIC: Towards a balanced facial image dataset for automatic ICAO compliance verification

Ensuring compliance with ISO/IEC and ICAO standards for facial images in machine-readable travel documents (MRTDs) is essential for reliable identity verification, but current manual inspection methods are inefficient in high-demand environments. This paper introduces the DFIC dataset, a novel comprehensive facial image dataset comprising around 58,000 annotated images and 2706 videos of more than 1000 subjects, that cover a broad range of non-compliant conditions, in addition to compliant portraits. Our dataset provides a more balanced demographic distribution than the existing public datasets, with one partition that is nearly uniformly distributed, facilitating the development of automated ICAO compliance verification methods. Using DFIC, we fine-tuned a novel method that heavily relies on spatial attention mechanisms for the automatic validation of ICAO compliance requirements, and we have compared it with the state-of-the-art aimed at ICAO compliance verification, demonstrating improved results. DFIC dataset is now made public (https://github.com/visteam-isr-uc/DFIC) for the training and validation of new models, offering an unprecedented diversity of faces, that will improve both robustness and adaptability to the intrinsically diverse combinations of faces and props that can be presented to the validation system. These results emphasize the potential of DFIC to enhance automated ICAO compliance methods but it can also be used in many other applications that aim to improve the security, privacy, and fairness of facial recognition systems.

cs.CV↗

arXiv2025

Second Competition on Presentation Attack Detection on ID Card

This work summarises and reports the results of the second Presentation Attack Detection competition on ID cards. This new version includes new elements compared to the previous one. (1) An automatic evaluation platform was enabled for automatic benchmarking; (2) Two tracks were proposed in order to evaluate algorithms and datasets, respectively; and (3) A new ID card dataset was shared with Track 1 teams to serve as the baseline dataset for the training and optimisation. The Hochschule Darmstadt, Fraunhofer-IGD, and Facephi company jointly organised this challenge. 20 teams were registered, and 74 submitted models were evaluated. For Track 1, the "Dragons" team reached first place with an Average Ranking and Equal Error rate (EER) of AV-Rank of 40.48% and 11.44% EER, respectively. For the more challenging approach in Track 2, the "Incode" team reached the best results with an AV-Rank of 14.76% and 6.36% EER, improving on the results of the first edition of 74.30% and 21.87% EER, respectively. These results suggest that PAD on ID cards is improving, but it is still a challenging problem related to the number of images, especially of bona fide images.

cs.CV↗

arXiv2025

One Giant Leap for Womankind: First Menstrual Cups Tested in Space Flight Conditions

In the early days of space exploration, when Sally Ride was offered 100 tampons for a week-long mission, menstrual medical devices first began to be used in space conditions. Since then, hormonal menstrual suppression has become the preferred method for managing menstruation in space, offering significant advantages. However, this is not an option for astronauts who choose to menstruate. The lack of sustainable menstrual technologies will pose challenges for long-duration missions to the Moon and Mars, where astronauts may spend years in space. The AstroCup mission represents the first effort to test menstrual cups in spaceflight, evaluating their durability and functionality. Through material integrity tests and functional assessments using a rheological analogue of human blood, we demonstrate the resilience of menstrual cups and discuss their implications for sustainable menstrual management in future lunar and Martian missions.

physics.med-ph↗

arXiv2023

EFaR 2023: Efficient Face Recognition Competition

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

cs.CV↗

arXiv2022

Analytical and numerical modeling of reconfigurable reflecting metasurfaces with capacitive memory

In this article, we develop analytical-numerical models for reconfigurable reflecting metasurfaces formed by chessboard-patterned arrays of metallic patches. These patch arrays are loaded with varactor diodes in order to enable surface impedance and reflection phase control. Two types of analytical models are considered. The first model based on the effective medium approach is used to predict the metasurface reflectivity. The second model is the Bloch wave dispersion model for the same structure understood as a two-dimensional transmission line metamaterial. The latter model is used to study ways to suppress parasitic resonances in finite-size beamforming metasurfaces. We validate the developed analytical models with full-wave numerical simulations. Finally, we outline a design of the metasurface control network with capacitive memory that may allow for independent programming of individual unit cells of the beamforming metasurface.

physics.app-ph↗