@inproceedings{ChangSchmelzerKoppetal.2019,
  author    = {Chang, Wan-Jung and Schmelzer, Miriam and Kopp, Florian and Hsu, Chia-Hao and Su, Jian-Ping and Chen, Liang-Bi and Chen, Ming-Che},
  title     = {A Deep Learning Facial Expression Recognition based Scoring System for Restaurants},
  booktitle = {International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 11-13 February 2019, Okinawa, Japan},
  isbn      = {978-1-5386-7822-0},
  doi       = {10.1109/ICAIIC.2019.8668998},
  institution = {Fakult{\"a}t Elektrotechnik und Informationstechnik},
  pages     = {4},
  year      = {2019},
  abstract  = {Recently, the popularity of automated and unmanned restaurants has increased. Due to the absence of staff, there is no direct perception of the customers' impressions in order to find out what their experiences with the restaurant concept are like. For this purpose, this paper presents a rating system based on facial expression recognition with pre-trained convolutional neural network (CNN) models. It is composed of an Android mobile application, a web server, and a pre-trained AI-server. Both the food and the environment are supposed to be rated. Currently, three expressions (satisfied, neutral and disappointed) are provided by the scoring system.},
  language  = {en}
}