/Facial_Analysis_with_Mask_Detection

AI-powered tool for Facial Attribute Analysis. It accurately predicts age, gender, race, and emotions for unmasked faces. Enhanced to detect masked faces, ensuring reliable analysis in real-world scenarios. Utilizes DeepFace for in-depth insights.

Primary LanguageJupyter NotebookMIT LicenseMIT

🕵️‍♂️ Facial Attribute Analysis with Mask Detection

Facial Attribute Analysis with Mask Detection

🔍 Overview

In the domain of facial recognition, our project stands out by integrating mask detection with deep learning-powered facial attribute analysis. We tackle the contemporary issue of obscured facial features due to mask-wearing, ensuring our algorithm remains effective in today’s mask-prevalent society. Our solution, utilizing the advanced InceptionV3 architecture along with the comprehensive DeepFace library, is capable of identifying faces in a variety of settings, discerning masked from unmasked individuals, and further analyzing unmasked faces for age, gender, race, and emotion.

🎯 Objectives

The key milestones in our project included:

  • Mask Detection Model Development: Developing a model to identify if faces in images are masked, using our dataset and InceptionV3's transfer learning capabilities for enhanced accuracy.
  • Facial Analysis with Mask Detection Integration: Combining mask detection with DeepFace-driven facial analysis to accurately annotate unmasked faces with attributes such as age, gender, race, and emotion.
  • Real-World Application Demo: Showcasing the model's effectiveness in a real-world scenario, demonstrating facial analysis and mask detection on a sample video.

🎬 Demo

Facial Attribute Analysis with Mask Detection in Action:

Demo Video

📁 File Descriptions

  • Facial_Attribute_Analysis_with_Mask_Detection.ipynb: The Jupyter notebook detailing our journey from data prep to model evaluation.
  • test_image/: Test image showcasing the model's generalization capability.
  • README.md: You're currently reading this file! Provides an overview and useful information about the project.

🚀 Instructions for Local Execution

  1. Clone this Repository: First, clone this repo to your local machine.
  2. Download the Dataset: Access the dataset for mask detection from Kaggle. Download and extract it to a known directory on your machine.
  3. Update Dataset Path: Open the Facial_Attribute_Analysis_with_Mask_Detection.ipynb notebook and update the dataset path to the location where you extracted the dataset.
  4. Setup Dependencies: Ensure that all necessary Python libraries are installed.
  5. Execution: Run all cells in the notebook to see the results.

🔗 Additional Resources

  • 🌐 Kaggle Notebook: Interested in a Kaggle environment? Explore the notebook here.
  • 📹 Input Video Data: Access the raw and modified video here.
  • 🎥 Project Demo: Watch the live demonstration of this project on YouTube.
  • 🤝 Connect on LinkedIn: Have questions or looking for collaboration? Let's connect on LinkedIn.