With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.

As AI systems increasingly make high-stakes decisions affecting human lives, the question of computational feasibility in implementing contestability mechanisms has emerged as a critical technical challenge. While the field of AI contestability remains in its infancy with limited theoretical frameworks, the practical question persists: which components of proposed contestability mechanisms can actually be implemented with current technical methods? This paper investigates computational feasibility through systematic cross-domain empirical analysis using a structured contestability framework as our testable hypothesis. We examine operational AI systems across security, legal, financial, and medical domains to assess the technical implementability of contestability requirements across diverse architectures and decision contexts. Our analysis reveals a three-tier taxonomy of implementation feasibility: fully computable components, partially computable elements, and non-computable aspects. Through systematic technical evaluation, we demonstrate that significant portions of the contestability framework requirements face substantial implementation challenges with current methods. The findings provide an empirical foundation for understanding technical boundaries of contestability implementation and identify specific areas where current AI system capabilities fall short of theoretical requirements, offering concrete guidance for both framework designers and system implementers.