With the growing production and access of resources managed by sensor-rich ubiquitous devices, such as photos, videos, and services, critical issues arise from access control point of view. Sensor-rich devices offer users new opportunities for creating, accessing, and sharing resources from anywhere and at anytime, interacting dynamically with other surrounding devices, users, and the environment. In this scenario, resources could be automatically annotated with information characterizing situations (context) to facilitate retrieval, organization, and enrichment of their contents. Moreover, resources may be available by users only on a specific situation (e.g., location and season), such as surveys asking visitors about touristic attractions while visiting them. However, existing ubiquitous applications do not exploit contextual and social annotation as central concept for defining access control policies to protect resources. Therefore, this paper proposes a context-aware rule-based access control model for protecting ubiquitous resources, presenting a prototype that implements this model, a case study based on an ubiquitous survey application, as well as performance evaluation results that demonstrate the effectiveness and scalability of the proposed solution.

Soft biometric information extracted from a human body (e.g., height, gender, skin color, hair color, and so on) is ancillary information easily distinguished at a distance but it is not fully distinctive by itself in recognition tasks. However, this soft information can be explicitly fused with biometric recognition systems to improve the overall recognition when confronting high variability conditions. One significant example is visual surveillance, where face images are usually captured in poor quality conditions with high variability and automatic face recognition systems do not work properly. In this scenario, the soft biometric information can provide very valuable information for person recognition. This paper presents an experimental study of the benefits of soft biometric labels as ancillary information based on the description of human physical features to improve challenging person recognition scenarios at a distance. In addition, we analyze the available soft biometric information in scenarios of varying distance between camera and subject. Experimental results based on the Southampton multibiometric tunnel database show that the use of soft biometric traits is able to improve the performance of face recognition based on sparse representation on real and ideal scenarios by adaptive fusion rules.