Face-Recognition-System-in-Python-using-FaceNet

🚀 Image Classification from Video using Computer Vision and Deep Learning

Project Overview

Dive into the captivating realm of computer vision and artificial intelligence with our Image Classification from Video project! 🎥🤖 This project is more than just recognizing faces; it's an adventure into the heart of cutting-edge technologies. From extracting frames to training models, this project is your ticket to the future of computer vision.

✨ Key Features

• Video Processing: Swiftly download and process videos using Python scripts.
• Face Detection: Harness the power of Haar Cascade Algorithm to extract faces from images and videos.
• Deep Learning Magic: Enchanting pre-trained FaceNet models and the mystical VGGFace architecture for precise face recognition.
• Modular Mastery: Explore organized modular code for a seamless understanding and effortless customization experience.
• Data Visualization: Peer into the world of embeddings and model predictions with mesmerizing visualizations.
• Google Colab Integration: Experience the magic of Google Colab for efficient and cloud-powered model training.

🌟 Data Description

Our dataset is a collection of frames from the beloved sitcom show, Friends. Characters like Rachel, Chandler, Phoebe, Monica, and Ross are the stars of this dataset. With a total of 35 images (7 per person for training and 15 for testing), this dataset forms the bedrock of our image classification odyssey.

🛠️ Tech Stack

• Language: Python (Version 3.6.2)
• Libraries: OpenCV, scikit-learn, numpy, os, pytube, scikit_image, skimage, keras, tensorflow
  • OpenCV: Your trusty sidekick for image and video processing.
  • Haar Cascade Algorithm: The mystical spell for accurate face extraction.
  • Pre-trained FaceNet Model: Unleashing the power of embeddings for face recognition.
  • VGGFace Architecture: Deep learning sorcery for the most precise identification spells.
  • Google Colab: Your enchanted notebook for cloud-powered, robust models.

🏰 Project Structure

• input: The treasure chest containing training and validation data, downloaded videos, and extracted frames.
• src: The spellbook, housing the core modular codebase for video processing, face extraction, and model training.
• output: The magical repository for predicted frames and model outputs.
• prebuilt_models: The ancient scrolls containing pre-trained models and your very own enchanted, trained models.
• lib: The magical library filled with reference Jupyter notebooks.
• requirements.txt: The potion recipe listing all the magical ingredients. Use pip install -r requirements.txt to concoct your magic potion!

⚡ How to Use

1. Clone the Repository:

git clone https://github.com/your-username/image-classification-from-video.git

2. Install Dependencies:

pip install -r requirements.txt

3. Explore the Jupyter Notebooks: Dive into the src folder and explore the modular Jupyter notebooks for detailed insights.

Here’s a polished and professional version to suit the tone of a professional competition entry:

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Contact Information

Got questions, ideas, or just want to geek out over AI and data? Here's where you can find me:

📧 Email: vidhiwaghela99@gmail.com
💻 GitHub: Vidhi1290
📊 Kaggle: vidhikishorwaghela
🔗 LinkedIn: Vidhi Waghela
📝 Medium: Data Science Meets Cyber Security
📸 Instagram: vidhi_waghela__
🐦 X (Twitter): @VidhiWaghela

Let’s connect and turn great ideas into even greater realities—because why should brilliance ever work alone? 🚀