Candidate:
João Ferreira de Carvalho Castro Nunes
Date, Time and Location:
12th December 2025, at 14:00, in Sala de Atos of the Faculdade de Engenharia da Universidade do Porto
President of the Jury:
Pedro Nuno Ferreira da Rosa da Cruz Diniz, Full Professor at the Department of Informatics Engineering of the Faculdade de Engenharia da Universidade do Porto
Members:
Carlos Miguel Fernandes Quental (PhD), Assistant Professor at the Department of Mechanical Engineering, Instituto Superior Técnico, Universidade de Lisboa;
Hugo Pedro Martins Carriço Proença (PhD), Full Professor at the Department of Computer Science, Universidade da Beira Interior;
João Manuel Ribeiro da Silva Tavares (PhD), Full Professor at the Department of Mechanical Engineering, Faculdade de Engenharia, Universidade do Porto (Supervisor);
Luís Paulo Gonçalves dos Reis (PhD), Associate Professor with Habilitation at the Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.
The thesis was co-supervised by Pedro Miguel do Vale Moreira (PhD), Full Professor at the Instituto Politécnico de Viana do Castelo.
Abstract:
Human gait analysis provides critical information on biomechanical function, clinical assessment, and biometric recognition, but achieving accurate and reproducible motion understanding under real-world variability remains a major challenge. Traditional motion capture techniques are dependent on expensive infrastructure and controlled environments, which limit scalability and realworld validity. This thesis addresses these limitations by developing computational methodologies that exploit both RGB and depth information to enable robust, efficient, and fully automatic gait analysis using consumer-grade sensors. The research followed a structured trajectory that encompasses dataset creation, representation design, and methodological innovation. First, an extensive review and comparative analysis of existing vision- and depth-based gait datasets identified gaps in modality diversity, annotation quality, and accessibility. To address these issues, the Gait Recognition Image and Depth Dataset (GRIDDS) was designed, acquired, and publicly released. GRIDDS provides synchronized RGB, depth, silhouette, and 3D skeletal data from 35 participants recorded under controlled conditions, establishing one of the first standardized multi-modal benchmarks for gait analysis and recognition. Building on this foundation, two novel computational gait representations were introduced that fuse two-dimensional appearance cues with three-dimensional skeletal structure to increase robustness to viewpoint, clothing, and carried-object variations. These Gait Skeleton Image (GSI) variants (joint-based and line-based) were integrated within deep learning frameworks and evaluated through extensive experiments, demonstrating competitive and, under certain circumstances, superior performance compared with established appearance-based methods across multiple datasets and covariate conditions. Finally, new methods for gait silhouette interpolation were introduced, combining deterministic geometric reasoning (BRIEF) and bidirectional deep learning (BiSINet) to reconstruct missing frames and enhance temporal coherence. The proposed interpolation techniques significantly improved downstream recognition accuracy and demonstrated strong generalization across datasets and frame-rate conditions. Collectively, the contributions of this work, which span multi-modal data acquisition, robust gait representation learning, and temporal reconstruction, advance the scientific and technological frontiers of human gait analysis, promoting reproducibility, accessibility, and applicability in both clinical and computer vision domains.