Overview

This project implements a deep learning-based solution for real-time face emotion detection using Convolutional Neural Networks (CNN). Emotion detection plays a crucial role in various applications such as human-computer interaction, market research, and mental health monitoring. By leveraging the power of CNNs, this project aims to accurately recognize and classify emotions from facial expressions captured in images or video streams.

Key Features

CNN Architecture: Utilizes a deep CNN architecture optimized for facial feature extraction and emotion classification. Real-time Detection: Provides real-time emotion detection capabilities suitable for interactive applications and systems. Multi-class Classification: Supports multi-class emotion classification, including common emotions such as happiness, sadness, anger, surprise, fear, and disgust. Pre-trained Models: Offers pre-trained CNN models for quick deployment and easy integration into existing projects. Customizable Training: Enables fine-tuning and customization of CNN models to adapt to specific use cases and datasets. Data Augmentation: Implements data augmentation techniques to enhance model robustness and generalization. Graphical User Interface (GUI): Includes a user-friendly GUI for easy interaction and visualization of emotion detection results. Compatibility: Compatible with both image and video inputs, supporting various formats and resolutions.

Usage After installation, refer to the usage guide for instructions on how to use the face emotion detection system, including running inference on images or video streams.