Abstract
This paper introduces a new recommendation system for museums able to profile the visitors and propose them the most suitable exhibition path accordingly, to improve visitors’ satisfaction. It consists of an interactive touch screen totem, which implements a USB camera and exploits Convolutional Neural Network to perform facial coding to measure visitors’ emotions and estimate their age and gender. Based on the detected level of emotional valence, the system associates visitors with a profile and suggests them to visit a selection of five works of art, following a specific itinerary. An extensive experimentation lasting 2 months has been carried out at the Modern Art Museum “Palazzo Buonaccorsi” of Macerata. Results evidence that the proposed system can create an interactive and emotional link with the visitors, influencing their mood in the Pre-Experience phase and in the subsequent Post-Experience phase. In particular, they highlight that the proposed system, which aims at acting as emotional leverage, has been able to improve the positiveness of the emotions experienced by the visitors.
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Acknowledgement
We thank the Musei Civici di Macerata, and in particular the director, Dr. Giuliana Pascucci, for her support in the experimentation at Palazzo Buonaccorsi. This work is supported by the Marche Region under the POR-FESR 2014–2020 program, project C.O.M.E., involving the companies Cherry Merry Lab, Marchingegno and Grottini Lab.
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Altieri, A., Ceccacci, S., Giraldi, L., Leopardi, A., Mengoni, M., Talipu, A. (2021). Affective Guide for Museum: A System to Suggest Museum Paths Based on Visitors’ Emotions. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Design Methods and User Experience. HCII 2021. Lecture Notes in Computer Science(), vol 12768. Springer, Cham. https://doi.org/10.1007/978-3-030-78092-0_35
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