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.
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.
@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.
},
}
Generated by bib2html.pl (written by Patrick Riley, Lotzi Boloni ) on Sun Mar 03, 2024 18:41:16