Face identification with CNN + TPE using Keras
NOTE: This repository is archived and will no longer be updated.
This repository contains an implementation of the Triplet Probabilistic Embedding for Face Verification and Clustering paper.
shell script git clone https://github.com/meownoid/face-identification-tpe.git cd face-identification-tpe python -m pip install -r requirements.txt
NOTE: Pre-trained model was trained using very small dataset and achieves poor performance. It can't be used in any real-world application and is intended for education purposes only.
To start application with the pre-trained weights download all assets and put them to the
modeldirectory (default path) or to the any other directory.
Then you can start the application.
shell script python application.py
If you placed assets to the other directory, specify path with the
shell script python application.py --model-path /path/to/assets/
NOTE: Training code was written a long time ago and have a lot of hard-coded constants in it. Using it now on new dataset will be very difficult, so please, don't try. You can read it and use it as a reference or you can just use CNN and TPE definitions and write custom training code.
I'm leaving this here just for the sake of history.
shape_predictor_68_face_landmarks.datfrom here and put them to the
Place train, test and evaluation (named
dev) data to the
datafolder using following structure.
data\ dev\ person_0\ 1.jpg 2.jpg ... person_1\ 1.jpg 2.jpg ... ... test\ person_0\ 1.jpg 2.jpg ... person_1\ 1.jpg 2.jpg ... ... train\ person_0\ 1.jpg 2.jpg ... person_1\ 1.jpg 2.jpg ... ...
All images in the
testdirectories must contain faces of the same person.