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Facial Emotion Recognition with Deep Learning

This project is a deep learning-based system for real-time facial emotion recognition. It detects and classifies emotions from facial expressions, offering valuable insights for applications in human-computer interaction, mental health monitoring, and user sentiment analysis.

Problem Statement

Understanding emotions through facial expressions enhances human-computer interactions, making technology more responsive to user emotions. This project addresses this by providing an automated solution to detect emotions in real-time, which is beneficial in fields like:

  • Mental Health Monitoring: Detect emotional cues that could indicate distress or other mental health issues.
  • Customer Sentiment Analysis: Understand user feedback through facial expressions, valuable for market research and user experience testing.
  • Enhanced Human-Computer Interaction: Improve responsiveness in virtual assistants, chatbots, and other interfaces by recognizing users' emotional states.

How It Works

The project contains two main scripts:

  1. Model Training (train.py): This script trains a deep learning model on a labeled dataset of facial images to classify various emotions. The model learns from distinct facial expressions, improving its generalization to accurately identify real-world emotions.

  2. Real-Time Detection (test.py): This script uses the camera to detect faces in real time, applying a pre-trained model to classify the emotion displayed. It uses the haarcascade.xml file for face detection and displays the recognized emotion directly on the screen.

  3. Advanced Segmentation with K-means Clustering: The project also explores using K-means clustering for facial region segmentation. This approach enhances emotion recognition accuracy by focusing on specific facial features.

Key Features

  • Real-Time Emotion Detection: Classifies emotions in live video feeds.
  • Accurate Facial Region Segmentation: Uses K-means clustering to isolate facial features, enhancing model performance.
  • Multiple Applications: Useful in fields ranging from mental health to customer experience analysis.

Getting Started

Prerequisites

Make sure you have Python installed along with the following libraries:

pip install -r requirements.txt