Emotion Detection with CNNs

About the Project

Emotion Detection with CNNs is a deep learning project that leverages Convolutional Neural Networks to recognize and classify human emotions from facial images. The model is trained to identify seven distinct emotional classes, providing a comprehensive emotion recognition system.

Built with Keras and utilizing OpenCV for image processing, the application can process both static images and real-time video feeds, making it suitable for various applications from user experience research to interactive systems. The project demonstrates the power of deep learning in computer vision tasks.

Key Features

• 7 Emotion Classes: Comprehensive classification across happiness, sadness, anger, surprise, fear, disgust, and neutral
• CNN Architecture: Custom convolutional neural network designed for facial emotion recognition
• Real-time Processing: OpenCV integration for live emotion detection from camera feeds
• Custom Training Data: Model trained on curated dataset for robust emotion recognition
• Image Preprocessing: Automated face detection and normalization pipeline
• High Accuracy: Optimized model architecture achieving reliable classification results

Technical Highlights

The Convolutional Neural Network architecture is specifically designed to capture the subtle features that distinguish different emotional expressions. Multiple convolutional layers extract hierarchical features from facial images, from basic edges to complex patterns indicative of specific emotions.

OpenCV handles face detection and preprocessing, ensuring that input images are properly normalized and aligned before being fed into the CNN. This preprocessing step is crucial for maintaining consistent model performance across varying image conditions.

The model training process includes data augmentation techniques to improve generalization and prevent overfitting, resulting in a robust classifier capable of handling real-world facial expressions with varying lighting, angles, and backgrounds.