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 ...]
image_files Images to compare
-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
Author View Profile
A philosophy student cleverly disguised as a Coax Deep Learning engineer spending whole day, practically every day, experimenting with TensorFlow,Pytorch, and Caffe; dabbling with Python and C++; and drinking a wide variety of Coffee everyday.
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