Abstract
Food plays an important role in every human being’s life. Taking this as major objective several people are coming up with numerous new restaurants. Many of these restaurants are with less or no staff. Because of this reason, the rating of most of the restaurants is inaccurate. To overcome this problem and give a practical and exact rating to restaurants, this paper presents a Facial Expression Recognition-based Restaurant Scoring System with a pre-trained CNN model. By this model, customers can rate the food as well as the environment of the restaurants in three different expressions (Satisfied, Neutral, Disappointed). For implementing and using this system, we consider an application and server.
Keywords: Automated restaurants, convolutional neural network, facial expression, restaurant scoring, unmanned restaurants
REFERENCES
1. Hassansain Saeed, Ali Shouman, Mais Elfar, Mostafa Shabka, Shikharesh Majumdar, and Chung Horng-Lung, “Near- field communication sensors and cloud- based smart restaurant management system,” in Proceedings of the 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 686-691, 2016.
2. Florian Schroff, Dmitry Kalenichenko, and James Philbin, “FaceNet: a unified embedding for face recognition and clustering,” n Proceedingsof the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815-823, 2015.
3. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andretti, and Hartwig Adam, “MobileNets: efficient convolutional neural networks for mobile vision applications, ” arXiv preprint arXiv:1704.04861, 2017
4. Janne Tommola, Pedram Ghazi, Bishwo Adhikara, and Heikki Huttunen, “Real-time system for facial analysis,” in Proceedings of the 7th European Workshop on Visual Information Processing (EUVIP’18), 2018.
5. Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019). https://doi.org/10.1007/s10586-018-1757-3
6. Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communications 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z.
7. Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari, “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification ", International Journal of Recent Technology and Engineering., vol. 8, no. 4, pp. 1630-1634, Jun. 2019.
8. Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
9. V.Senthilkumar , K.Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
10. Dr.Vignesh Janarthanan, Dr.Venkata Reddy Medikonda,.Er.Dr.G.Manoj Someswar Proposal of a Novel Approach for Stabilization of the Image from Omni-Directional System in the case of Human Detection & Tracking “American Journal of Engineering Research (AJER)” vol 6 issue 11 2017
11. Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
12. Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
13. Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
14. Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
15. Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
16. Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
17. Ardra, S., & Viswanathan, A. (2012). A Survey on Detection and Mitigation of Misbehavior in Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).