Face Sentiment Prediction Using Deep Learning

EasyChair Preprint 13204

Abstract

This paper discusses the categorizationof real-time photographs depicting human facial emotions. We can gain from implementing this work in software in a few ways. Facial expression-based emotion identification is a rapidly developing topic with applications in a variety of fields, including psychology, marketing, healthcare, and human-computer interaction.The goal of this project is to employ machine learning techniques to create an intuitive graphical user interface (GUI) application for facial sentiment recognition. By leveraging algorithms for face detection and emotion classification, the application allows users to upload images containing human faces and receive real-time feedback on the emotions expressed in those faces. The project objectives include developing a GUI application, implementing machine learning algorithms for face detection and emotion classification, and providing real-time feedback to users. The research is driven by the growing need for automated systems that are able to recognize and react to human emotions.The significance lies in the potential for personalized user experiences, targeted marketing strategies, and mental health assessment tools. This project contributes to advancing the field of machine learning by exploring novel applications in emotion detection and human-computer interaction.

Keyphrases: Graphical User Interface, Human-Computer Interaction., Keywords: Emotion detection, facial expressions, machine learning

@booklet{EasyChair:13204,
  author    = {S.N Chandra Shekhar and Abhijith Bandari and Sai Prasanna Perumandla and Sunia Sultana},
  title     = {Face Sentiment Prediction Using Deep Learning},
  howpublished = {EasyChair Preprint 13204},
  year      = {EasyChair, 2024}}