Build a Deep Facial Recognition App - Kivy Computer Vision App with OpenCV and Tensorflow

Learn how to build a deep facial recognition application for authentication in this comprehensive tutorial video. Implement facial recognition and verification into your application using Deep Learning with TensorFlow, replicating the Siamese Neural Networks for One-shot Image Recognition paper. Integrate the trained model into a Kivy app for practical authentication. Cover essential steps including Kivy installation and setup, building a TensorFlow computer vision app, and performing facial verification. Follow along as the instructor guides you through creating custom layers, importing dependencies, building the app layout, and implementing key functions for preprocessing and verification. Explore performance tuning techniques, data augmentation, and final model testing to optimize your facial recognition system.

- Start
- Explainer
- Tutorial Start
- Whiteboard
- Setup App Folder
- Install Kivy
- Setup Validation Folder
- Create Custom Layer module
- Bring over h5 model
- Create faceid.py
- Import Dependencies
- Import Other Dependencies
- Build App Layout
- Build Update Function
- Migrate preprocessing Function
- Migrate verification Function
- Setup Webcam Saving
- Load Tensorflow Model
- Link Verification button to Function
- Testing the App
- Tuning the Verification and Detection Metrics
- Debugging Weird Detection Performance
- Performance Tuning
- Data Augmentation
- Adding more images to Dataset
- Bump up Shuffle buffer_size
- Evaluate on Whole Test Dataset
- Testing the Final Model