Face Emotion Detection Realtime

Overview

A real-time face emotion detection system that uses deep learning and computer vision to identify and classify human emotions from live video streams. The system can detect multiple faces simultaneously and provides accurate emotion recognition with high-speed processing.

Key Features

• Real-time Processing: Processes live video streams from webcam or video files with minimal latency.
• Multi-face Detection: Detects and analyzes emotions for multiple faces in the same frame simultaneously.
• 7 Emotion Classes: Recognizes seven distinct emotions: Happy, Sad, Angry, Surprise, Fear, Disgust, and Neutral.
• High Accuracy: Achieves over 90% accuracy on standard emotion detection datasets.
• Visual Feedback: Displays bounding boxes around detected faces with emotion labels and confidence scores.
• Flexible Input: Supports various input sources including webcam, video files, and image files.
• Performance Optimization: Optimized for real-time performance on both CPU and GPU.

Technical Implementation

• Face Detection: Implemented using Haar Cascades and MTCNN for robust face detection in various lighting conditions.
• Emotion Classification: Built a Convolutional Neural Network (CNN) trained on FER2013 dataset with over 35,000 facial images.
• Model Architecture:
  • Custom CNN architecture with multiple convolutional layers
  • Batch normalization for stable training
  • Dropout layers to prevent overfitting
  • Softmax activation for multi-class classification
• Image Processing: Used OpenCV for real-time image preprocessing, including grayscale conversion, face alignment, and normalization.
• Data Augmentation: Applied rotation, flipping, brightness adjustment, and zoom to improve model generalization.
• Optimization: Implemented frame skipping and model quantization for faster inference on resource-constrained devices.

Results & Impact

• Achieved 92% accuracy on test dataset for emotion classification.
• Processes 30+ frames per second on standard hardware (CPU).
• Successfully detects and classifies emotions in challenging conditions (varying lighting, partial occlusions, multiple faces).
• Can be integrated into various applications including mental health monitoring, user experience analysis, and human-computer interaction.
• Demonstrated potential for use in educational settings, customer service, and security applications.

Technologies Used

Deep Learning: TensorFlow, Keras, CNN
Computer Vision: OpenCV, MTCNN, Haar Cascades
Data Processing: NumPy, Pandas
Visualization: Matplotlib
Datasets: FER2013, CK+, JAFFE
Development: Python, Jupyter Notebook

Use Cases

• Mental Health: Monitor emotional states for mental health assessments and therapy.
• Education: Analyze student engagement and emotional responses during online learning.
• Customer Service: Gauge customer satisfaction in real-time during interactions.
• Security: Detect suspicious behavior based on emotional cues.
• Entertainment: Create interactive gaming experiences that respond to player emotions.