{"id":28363881,"url":"https://github.com/aryehky/visagecraft","last_synced_at":"2025-06-23T21:31:14.490Z","repository":{"id":136239832,"uuid":"604432328","full_name":"aryehky/VisageCraft","owner":"aryehky","description":"🔍 VisageCraft is a hands-on deep learning toolkit for facial recognition and image classification. 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧠 VisageCraft: Deep Vision with Faces \u0026 Features\n\n**VisageCraft** is a creative fusion of deep learning tools for mastering two key areas of computer vision:\n\n* **Face detection and identity recognition** powered by MTCNN and VGGFace.\n* **Transfer learning for image classification** using pretrained CNNs like VGG16 and ResNet.\n\n---\n\n## 📓 Interactive Notebooks\n\n| Notebook File           | Focus Area                   | Description                                                                           |\n| ----------------------- | ---------------------------- | ------------------------------------------------------------------------------------- |\n| `identity_finder.ipynb` | 😎 Face Recognition Pipeline | Detects and verifies identities using MTCNN and VGGFace with preprocessing workflows. |\n| `feature_learner.ipynb` | 🧠 CNN Transfer Classifier   | Leverages pretrained CNNs to classify new image sets with limited data.               |\n\n---\n\n## 🔍 Key Features\n\n### 👤 Facial Recognition\n\n* **MTCNN Detection**: Localizes facial features across lighting, angles, and expressions.\n* **VGGFace Embeddings**: Compares and verifies identity robustness.\n* **Preprocessing Suite**: Alignment, cropping, and normalization included for optimal inputs.\n\n### 🧠 Transfer Learning with CNNs\n\n* Fine-tunes VGG16, ResNet50 on new image categories.\n* Demonstrates fast adaptation with minimal training data.\n* Shows how to reuse high-performing feature extractors in new contexts.\n\n---\n\n## 🛠 Technology Stack\n\n* **Python**: Development and scripting\n* **TensorFlow / Keras**: Model training and deployment\n* **MTCNN**: Face detection library\n* **VGGFace**: Embedding model for identity verification\n* **OpenCV / Matplotlib**: Visual debugging and data inspection\n* **Scikit-learn**: Evaluation metrics and validation\n\n---\n\n## 📁 Project Structure\n\n```\nVisageCraft/\n├── identity_finder.ipynb     # Facial detection and recognition notebook\n├── feature_learner.ipynb     # CNN transfer learning for image classification\n└── README.md                 # This file\n```\n\n---\n\n## 🎯 Why This Project?\n\n**VisageCraft** offers an approachable way to:\n\n* Understand real-world facial recognition pipelines.\n* Learn transfer learning best practices with CNNs.\n* Explore deep learning concepts through direct, visual experimentation.\n\nWhether you're curious about AI-powered security, personalization apps, or classification tools, this project gives you the groundwork to build your own solutions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryehky%2Fvisagecraft","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faryehky%2Fvisagecraft","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryehky%2Fvisagecraft/lists"}