Oussama Zoubia (M.Sc.)

Facial recognition has always been a field of continuous development and research due to its usage in different areas such as security and robotics. It has gained even more popularity and interest by the researchers with the recent advancements in artificial intelligence and deep learning, which improved the robustness of facial recognition systems. In this paper, we focus on facial recognition using deep learning on small data sets with a limited number of individuals, for that we propose a local features based facial recognition approach that combines the robustness of feature extraction of CNN with the Harris corner detector. The experimental results of our proposed method surpassed the results of classical methods (LBP, Eigen Face, and Fisher Face) as well as recent works on Georgia Tech Face Database and AR Face Database and proved its efficiency and its robustness in different conditions including illumination variation, face pose variation, changes in facial expressions and face occlusions.

In the digital age, data has emerged as one of the most valuable assets across various sectors, including academia, industry, and healthcare. Effective data preservation involves the management of data to ensure its long-term accessibility and usability. Given the importance and sensitivity of data, the need for effective management is a crucial necessity. One of the big recent proposed approaches for data management is the FAIR Digital Objects (FDOs) which has emerged to revolutionize the field of data management and preservation. Central to this revolution is the alignment of FDOs with the FAIR principles (Findable, Accessible, Interoperable, Reusable), particularly emphasizing machine-actionability and interoperability across diverse data ecosystems. This paper presents ”FDO Manager,” a Minimum Viable Implementation of FDOs, tailored specifically for the use case and field of research artefacts such as datasets, publications, and code. The paper discusses the core ideas behind the FDO Manager, its architecture, usage and implementation details, as well as its potential impact, demonstrating a simple and abstract implementation of FDOs in the research realm.