Américo José Pereira

Abstract
Face recognition technology, despite its advancements and increasing accuracy, still presents significant challenges in explainability and ethical concerns, especially when applied in sensitive domains such as surveillance, law enforcement, and access control. The opaque nature of deep learning models jeopardises transparency, bias, and user trust. Concurrently, the proliferation of web applications presents a unique opportunity to develop accessible and interactive tools for demonstrating and analysing these complex systems. These tools can facilitate model decision exploration with various images, aiding in bias mitigation or enhancing users’ trust by allowing them to see the model in action and understand its reasoning. We propose an explainable face recognition web application designed to support enrolment, identification, authentication, and verification while providing visual explanations through pixel-wise importance maps to clarify the model’s decision-making process. The system is built in compliance with the European Union General Data Protection Regulation, ensuring data privacy and user control over personal information. The application is also designed for scalability, capable of efficiently managing large datasets. Load tests conducted on databases containing up to 1,000,000 images confirm its efficiency. This scalability ensures robust performance and a seamless user experience even with database growth. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
We propose a unified architecture for visual scene understanding, aimed at overcoming the limitations of traditional, fragmented approaches in computer vision. Our work focuses on creating a system that accurately and coherently interprets visual scenes, with the ultimate goal to provide a 3D virtual representation, which is particularly useful for applications in virtual and augmented reality. By integrating various visual and semantic processing tasks into a single, adaptable framework, our architecture simplifies the design process, ensuring a seamless and consistent scene interpretation. This is particularly important in complex systems that rely on 3D synthesis, as the need for precise and semantically coherent scene descriptions keeps on growing. Our unified approach addresses these challenges, offering a flexible and efficient solution. We demonstrate the practical effectiveness of our architecture through a proof-of-concept system and explore its potential in various application domains, proving its value in advancing the field of computer vision.

Abstract
The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an end-to-end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.

Abstract
The problem of gathering sufficiently representative data, such as those about human actions, shapes, and facial expressions, is costly and time-consuming and also requires training robust models. This has led to the creation of techniques such as transfer learning or data augmentation. However, these are often insufficient. To address this, we propose a semi-automated mechanism that allows the generation and editing of visual scenes with synthetic humans performing various actions, with features such as background modification and manual adjustments of the 3D avatars to allow users to create data with greater variability. We also propose an evaluation methodology for assessing the results obtained using our method, which is two-fold: (i) the usage of an action classifier on the output data resulting from the mechanism and (ii) the generation of masks of the avatars and the actors to compare them through segmentation. The avatars were robust to occlusion, and their actions were recognizable and accurate to their respective input actors. The results also showed that even though the action classifier concentrates on the pose and movement of the synthetic humans, it strongly depends on contextual information to precisely recognize the actions. Generating the avatars for complex activities also proved problematic for action recognition and the clean and precise formation of the masks.

Abstract
With the advent of self-driving cars and the push by large companies into fully driverless transportation services, monitoring passenger behaviour in vehicles is becoming increasingly important for several reasons, such as ensuring safety and comfort. Although several human action recognition (HAR) methods have been proposed, developing a true HAR system remains a very challenging task. If the dataset used to train a model contains a small number of actors, the model can become biased towards these actors and their unique characteristics. This can cause the model to generalise poorly when confronted with new actors performing the same actions. This limitation is particularly acute when developing models to characterise the activities of vehicle occupants, for which data sets are short and scarce. In this study, we describe and evaluate three different methods that aim to address this actor bias and assess their performance in detecting in-vehicle violence. These methods work by removing specific information about the actor from the model's features during training or by using data that is independent of the actor, such as information about body posture. The experimental results show improvements over the baseline model when evaluated with real data. On the Hanau03 Vito dataset, the accuracy improved from 65.33% to 69.41%. On the Sunnyvale dataset, the accuracy improved from 82.81% to 86.62%.