Facial Expression Based Automated Restaurant Food Review System using CNN

Niazi Mahrab, S. Salim, Abdullah Ibne Ali, Israt Jahan Mim, R. Khan
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Abstract

A large amount of money is added every year to the economy through the restaurant business in a country. Nowadays, the restaurant business in Bangladesh has become very popular because of the increasing number of customers and high profit margins. Different people prefer various types of foods in the restaurant; moreover, they order food without knowing the quality and the taste of the food. There are a few restaurant review systems for customers in Bangladesh, they are mostly mobile application-based. As a result, the customer does not have any appropriate knowledge about the restaurant and the food. In this work, we tried to apply deep learning techniques for the restaurant and food review system by recognizing facial expressions with the help of convolutional neural network and the FER-2013 dataset, which is an open-source dataset. The experiment results show that the proposed technique performs satisfactorily with an accuracy of 81%. Finally, the efficiency of the system has been tested by using realtime images.
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使用 CNN 的基于面部表情的餐厅食品评论自动系统
在一个国家,每年都有大量的钱通过餐饮业注入经济。如今,由于顾客数量的增加和高利润率,孟加拉国的餐饮业变得非常受欢迎。不同的人喜欢餐馆里不同类型的食物;此外,他们在不知道食物的质量和味道的情况下点菜。孟加拉国有一些针对顾客的餐厅评论系统,它们大多是基于移动应用程序的。因此,顾客对餐厅和食物没有任何适当的了解。在这项工作中,我们尝试将深度学习技术应用于餐馆和食物评论系统,通过卷积神经网络和FER-2013数据集(一个开源数据集)来识别面部表情。实验结果表明,该方法具有良好的精度,准确率达81%。最后,利用实时图像验证了系统的有效性。
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