Zehtabian, SharareKhodadadeh, SiavashKim, KangsooBruder, GerdWelch, GregBölöni, LadislauTurgut, DamlaKulik, Alexander and Sra, Misha and Kim, Kangsoo and Seo, Byung-Kuk2020-12-012020-12-012020978-3-03868-112-01727-530Xhttps://doi.org/10.2312/egve.20201273https://diglib.eg.org:443/handle/10.2312/egve20201273Intelligent virtual agents have many societal uses, specifically in situations in which the presence of real humans would be prohibitive. In particular, virtual receptionists can perform a variety of tasks associated with visitor and employee safety, e.g., during the COVID-19 pandemic. In this poster, we present our prototype of a virtual receptionist that employs computer vision and meta-learning techniques to identify and interact with a visitor in a manner similar to that of a real human receptionist. Specifically we employ a meta-learning-based classifier to learn the visitors' faces from the minimal data collected during a first visit, such that the receptionist can recognize the same visitor during follow-up visits. The system also makes use of deep neural network-based computer vision techniques to recognize whether the visitor is wearing a face mask or not.Computing methodologiesIntelligent agentsObject identification10.2312/egve.202012739-10