🧠 Video-Based Emotion Detection Using Deep Learning
Advanced CNN-based facial emotion recognition system trained on FER2013 dataset, capable of detecting 7 different emotions from facial expressions in real-time video streams with 65% accuracy.
Project Resources
Project Overview
Developed a Convolutional Neural Network (CNN) based facial emotion recognition system trained on the FER2013 dataset. The model can detect 7 different emotions from facial expressions in real-time video streams.
Integrated with OpenCV for real-time video processing, the system achieves 65% accuracy in emotion classification, making it suitable for applications in customer behavior analysis, mental health monitoring, and human-computer interaction.
7 Emotion Classes Detected
The CNN model recognizes these facial expressions in real-time
Key Features
CNN Architecture
Deep learning model with multiple convolutional layers for accurate emotion classification
Real-time Processing
Live video stream analysis with OpenCV for instant emotion detection
FER2013 Dataset
Trained on comprehensive facial expression recognition dataset
65% Accuracy
High accuracy rate suitable for real-world applications and research
Technologies & Tools
Python 3.x
Core programming language
TensorFlow & Keras
Deep learning frameworks
OpenCV
Computer vision library
CNN Model
Convolutional Neural Network
Applications
Customer Behavior Analysis
Analyze customer reactions to products and services
Mental Health Monitoring
Track emotional states for therapy and counseling
Human-Computer Interaction
Enhance UI/UX with emotion-aware systems
Educational Technology
Adapt learning content based on student emotions