FACENET FOR FACE RECOGNITION

WHAT IS IT?

Facenet is a system built by Florian Schroff, Dmitry Kalenichenko, James Philbin. They wrote a paper about it as well. It directly learns a mapping from face images into a compact Euclidean space where distances directly correspond to a measure of face similarity. Once these embeddings are created then procedures like face recognition and verification can be done utilising these embeddings as features. Facenet creates a 128-dimensional embedding from images and inserts them into a feature space, in such a way, that the squared distance between all faces, regardless of the imaging conditions, of the same identity, is small, whereas the squared distance between a pair of face images from distinct characters is large.

HOW TO USE?

To run the script python run.py br1.jpg br2.jpg For help - python run.py -h

usage: run.py [-h] [--image_size IMAGE_SIZE] [--margin MARGIN]  image_files [image_files ...]

positional arguments:

image_files           Images to compare

optional arguments:

-h, --help  show this help message and exit --image_size     IMAGE_SIZE            Image size (height, width) in pixels. --margin            MARGIN                   Margin for the crop around the bounding box (height,width) in pixels.

WHAT ARE THE REQUIREMENTS?

To get all the requirements and dependencies installed run the command For GPU pip install -r gpu_requirements.txt For CPU pip install -r cpu_requirements.txt