The Quantified and Customised Museum

Mobile and wearable devices provide a range of new tools and approaches to measure the output metrics of the human body, especially in the medical and fitness realms. Cultural institutions similarly are drawing on a range of digital technologies to better understand the neural processes associated with visitor appreciation of artefacts. Harnessing data about how artworks and objects are experienced can be derived from measurable physical observations, such as an individual’s facial geometry, heartbeat, or retina movements. The Peabody Essex Museum in Salam, Massachusetts is used as a case study to explore the onsite quantification of the museum body. This example is placed in conversation with Google’s face match app, which uses computer vision to link user selfies with cultural collections. The discussion focuses on the evolution of a more quantified modality of cultural engagement, which places greater emphasis on the collection of data as an indicator of the quality of a museum visit.

Face Match for Family Reunification

Despite the many advances in face recognition technology, practical face detection and matching for unconstrained images remain challenging. A real-world Face Image Retrieval (FIR) system is described in this paper. It is based on optimally weighted image descriptor ensemble utilized in single-image-per-person (SIPP) approach that works with large unconstrained digital photo collections. The described visual search can be deployed in many applications, e.g. person location in post-disaster scenarios, helping families reunite quicker. It provides efficient means for face detection, matching and annotation, working with images of variable quality, requiring no time-consuming training, yet showing commercial performance levels.