A Deep Learning Facial Expression Recognition based Scoring System for Restaurants
- 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.
| Author: | Wan-Jung Chang, Miriam Schmelzer, Florian Kopp, Chia-Hao Hsu, Jian-Ping Su, Liang-Bi Chen, Ming-Che Chen |
|---|---|
| DOI: | https://doi.org/10.1109/ICAIIC.2019.8668998 |
| ISBN: | 978-1-5386-7822-0 |
| ISBN: | 978-1-5386-7823-7 |
| Parent Title (English): | International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 11-13 February 2019, Okinawa, Japan |
| Publisher: | IEEE |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of Publication: | 2019 |
| Release Date: | 2024/07/09 |
| Tag: | Automated Restaurants; Deep Learning; Facial Recognition; Restaurant Scoring; Unmanned Restaurant; Internet of Things; IoT |
| Page Number: | 4 |
| Institutes: | Fakultät Elektrotechnik und Informationstechnik |
| Fakultät Informatik | |
| Open Access?: | Nein |
| Licence (German): |  Urheberrechtlich geschützt |
