Emotion Detection Computer Vision Project

Emotion Detection Model for Facial Expressions

Project Description:

In this project, we developed an Emotion Detection Model using a curated dataset of 715 facial images, aiming to accurately recognize and categorize expressions into five distinct emotion classes. The emotion classes include Happy, Sad, Fearful, Angry, and Neutral.

Objectives:

• Train a robust machine learning model capable of accurately detecting and classifying facial expressions in real-time.
• Implement emotion detection to enhance user experience in applications such as human-computer interaction, virtual assistants, and emotion-aware systems.

Methodology:

1. Data Collection and Preprocessing:

   • Assembled a diverse dataset of 715 images featuring individuals expressing different emotions.
   • Employed Roboflow for efficient data preprocessing, handling image augmentation and normalization.

2. Model Architecture:

   • Utilized a convolutional neural network (CNN) architecture to capture spatial hierarchies in facial features.
   • Implemented a multi-class classification approach to categorize images into the predefined emotion classes.

3. Training and Validation:

   • Split the dataset into training and validation sets for model training and evaluation.
   • Fine-tuned the model parameters to optimize accuracy and generalization.

4. Model Evaluation:

   • Evaluated the model's performance on an independent test set to assess its ability to generalize to unseen data.
   • Analyzed confusion matrices and classification reports to understand the model's strengths and areas for improvement.

5. Deployment and Integration:

   • Deployed the trained emotion detection model for real-time inference.
   • Integrated the model into applications, allowing users to interact with systems based on detected emotions.

Results: The developed Emotion Detection Model demonstrates high accuracy in recognizing and classifying facial expressions across the defined emotion classes. This project lays the foundation for integrating emotion-aware systems into various applications, fostering more intuitive and responsive interactions.