Emotion Detection

Project Description

The Emotion Detection project aims to build a machine learning model that can analyze facial expressions and accurately classify them into different emotional categories such as happiness, sadness, anger, surprise, etc. This project leverages computer vision and deep learning techniques to recognize human emotions from images or video streams.

Key Steps

Data Collection: Gather a dataset of facial images labeled with different emotions (e.g., FER2013, CK+). Ensure the dataset contains diverse facial expressions across different genders, ages, and ethnicities. Data Preprocessing: Preprocess the images by resizing, normalizing, and converting them to grayscale. Apply data augmentation techniques like rotation, zoom, and flipping to increase the dataset's diversity and robustness. Model Building: Use Convolutional Neural Networks (CNNs) to build a deep learning model that can detect and classify emotions. Train the model on the preprocessed dataset and evaluate its performance using a validation set. Model Evaluation: Evaluate the model’s performance using metrics like accuracy, precision, recall, and confusion matrix. Perform hyperparameter tuning and apply techniques like early stopping and dropout to improve the model's generalization. Real-Time Emotion Detection: Integrate the model with a webcam or video feed to perform real-time emotion detection. Use OpenCV to capture and process live video frames, feeding them into the trained model for prediction. Deployment: Deploy the emotion detection model in a web or mobile application for real-time use cases such as customer service, virtual assistants, or mental health monitoring.

model acc and loss