Build a Facial Recognition App - Deep Learning Project - Paper2Code Series

Learn to build a complete facial recognition application from scratch using deep learning techniques in this comprehensive tutorial series. Master the entire development pipeline starting from understanding the underlying research paper through to deploying a real-time application. Collect and prepare facial image data using TensorFlow's data loading capabilities, then construct and train a Siamese neural network architecture specifically designed for facial recognition tasks. Implement the trained model to make accurate facial recognition predictions and integrate OpenCV for real-time video processing capabilities. Complete the project by building a user-friendly computer vision application using the Kivy framework that combines OpenCV and TensorFlow for live facial recognition functionality.

Syllabus

Build a Deep Facial Recognition App from Paper to Code // Part 1 // Deep Learning Project Tutorial
Build a Deep Facial Recognition App // Part 2 Collecting Data // Deep Learning Project Tutorial
Build a Deep Facial Recognition App // Part 3 - Preparing Data for Deep Learning // TF Dataloader
Build a Deep Facial Recognition App // Part 4 - Building a Siamese Neural Network // #Python
Build a Deep Facial Recognition App // Part 5 - Training a Siamese Neural Network // #Python
Build a Deep Facial Recognition App // Part 6 - Making Facial Recognition Predictions // #Python
Build a Deep Facial Recognition App // Part 7 - Real Time Predictions with OpenCV // #Python
Build a Deep Facial Recognition App // Part 8 - Kivy Computer Vision App with OpenCV and Tensorflow