This project was completed as a part of the Honors portion of the Convolutional Neural Networks Course on Coursera.
Credit to DeepLearning.AI and the Coursera platform for providing the course materials and guidance.
The primary objective of this project is to explore and distinguish between face recognition and face verification methods. Through hands-on implementation, I aim to tackle a face recognition problem using one-shot learning, a technique that requires just a single example per class for training. Additionally, I will apply the triplet loss function, which facilitates learning a network's parameters effectively for face recognition tasks.
As part of the project, I will demonstrate how to pose face recognition as a binary classification problem, thereby simplifying the approach to classifying faces accurately. To achieve this, I will map face images into 128-dimensional encodings using a pretrained model, enabling a more compact representation of facial features.
