Feedback Analyser through Facial Expressions

Abstract

Abstract—Feedback is an essential part of human connection and communication since it may used to express expression, feelings, and truth. This research paper works on the complex function that facial signals play in the interpretation and communication of information by offering a thorough overview and analysis of feedback through facial expressions. We investigate the brain systems that underlie facial feedback perception and response in humans, as well as the mechanisms behind face expressions, cultural variances, and their universality. Deep neural networks facial expression feedback analyzers are cur- rently in the starting stages of development, but they have the strength to solve a variety of issues with present human- computer interface systems .Feedback detection, for instance, is used to build more immersive entertainment experiences, more natural and educational systems, and more in customer service experiences, everywhere feedback is used. One of the main advantages of feedback analyzers using face expression by deep neural networks is that they can provide real time feedback. This is in way of natural feedback collection methods, such as surveys and interviews, which can be time consuming and expensive to administer. Real time feedback analyzer allows system developers to make rapid changes to their system in order to improve and develop the user experience. we have leveraged a custom trained CNN model, to accurately classify the seven different emotions of an human face that are availed in the dataset. The model has achieved an impressive accuracy of 83%. This model can be used in various applications. Index Terms—Convolutional Neural Networks, facial expres- sion, emotion, feedback, Resnet.