Ivido is a start-up company that aims at improving the communication and efficiency in health care. To do so, Ivido has developed a platform where health care professionals can create health courses for their patients, to stimulate them to become more involved in their own health care program. Because of lack of means to communicate, the client asked for a Patient Health Network, that will allow professionals, caregivers and patients to communicate together. Important characteristics of this Health Network are that users must be able to share documents, to communicate in private and in groups and to build their own network of connections

The amount of personal imagery kept on (mobile) devices is increasing by the day. Analysis and organization of these large collections of data are becoming increasingly important in the field of digital forensics, as they can aid in the search for legal evidence. The grouping of faces based on their identity is an important aspect as it provides an overview of the person in question and their connection with scenes, objects and other people. In this work, we propose a fuzzy approach to the hard partitioning problem of face clustering for the specific field of forensic investigations. We constructed a pipeline consisting of deep models for face detection and feature extraction, a method for transforming the resulting feature vectors to a graph representation and a graph-based clustering algorithm for the final partitioning. Focusing on the clustering step, we propose to assign face images to identity clusters using confidence values (rather than a hard cutoff) based on the average similarity with images present in the cluster relative to other clusters. Compared to existing methods, the approach is not only fuzzy but also embraces na¨ıve linking, and instead of transitively merging the links it uses a graph-based algorithm to produce the clusters. Furthermore, we propose an adapted version of the MaxMax algorithm because the original method only returned fuzzy results if weights were exactly equal. However, similarities between images are continuous, making it unsuitable for the case of face clustering. Evaluation of the performance on the Labeled Face in the Wild (LFW) dataset and the challenging IARPA JANUS Benchmark B (IJB-B) shows promising results comparable with state-ofthe-art face clustering algorithms.