S. Zehtabian, S. Khodadadeh, K. Kim, G. Bruder, G. F. Welch, L. Bölöni, and D. Turgut

An Automated Virtual Receptionist for Recognizing Visitors and Assuring Mask Wearing


Cite as:

S. Zehtabian, S. Khodadadeh, K. Kim, G. Bruder, G. F. Welch, L. Bölöni, and D. Turgut. An Automated Virtual Receptionist for Recognizing Visitors and Assuring Mask Wearing. In International Conference on Artificial Reality and Telexistence / Eurographics Symposium on Virtual Environmnents (ICAT-EGVE-2020), December 2020.

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Abstract:

Virtual receptionists have long been a desire for many circumstances in general. The specific circumstances associated with COVID-19 offer additional motivations for virtual receptionists, in particular associated with visitor and employee safety. 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 human receptionist. Specifically we employ a meta-learning-based classifier to learn the users' faces from the minimal data collected during a first visit, such that the receptionist can recognize the same user 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.

BibTeX:

@inproceedings{Zehtabian-2020-ICAT-EGVE,
   author = "S. Zehtabian and S. Khodadadeh and K. Kim and G. Bruder and G. F. Welch and L. B{\"o}l{\"o}ni and D. Turgut",
   title = "An Automated Virtual Receptionist for Recognizing Visitors and Assuring Mask Wearing",
   booktitle = "International Conference on Artificial Reality and Telexistence / Eurographics Symposium on Virtual Environmnents (ICAT-EGVE-2020)",
   year = "2020",
   month = "December",
   location = "Orlando, FLorida",
   abstract = {
    Virtual receptionists have long been a desire for many circumstances in general. The specific circumstances associated with COVID-19 offer additional motivations for virtual receptionists, in particular associated with visitor and employee safety.
    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 human receptionist.
    Specifically we employ a meta-learning-based classifier to learn the users' faces from the minimal data collected during a first visit, such that the receptionist can recognize the same user 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.
   },
}

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