facenet-pytorch VS CompreFace

Hijacking this comment because i've been working nonstop on my project thanks to your suggestion. I'm now using this https://github.com/derronqi/yolov8-face for face detection and still the old face_recognition for encodings. I'm clustering with dbscan and extracting frames with ffmpeg with -hwaccel on. I'm planning to try this: https://github.com/timesler/facenet-pytorch as it looks like it would be the fastest thing avaiable to process videos? Keep in mind i need to perform encoding other than just detection because i want to use DBscan (and later also facial recognition, but this might be done separately just by saving the encodings). let me know if you have any other suggestions, and thanks again for your help

For now, I am trying to evaluate and get the accuracy of the FaceNet module. Like this example on facenet-pytorch, getting the accuracy relies on this file (pairs.txt) provided by the official site. Format description below:

with some python knowledge you can try a two step procedure: 1) extract a number of frames per second, for example five frames (images, i.e. still frames) per second using opencv or ffmpeg 2) Using facenet: detect faces in frames and then classify them by comparing each image to a known image of the person you are looking for.

It's generally better to split the task into a multiple tasks. First I'd want to detect and extract faces. There are a number of pretrained models that you could use for that, e.g. https://github.com/timesler/facenet-pytorch, https://github.com/opencv/opencv/tree/master/data/haarcascades. Once you've extracted faces, you can train a facial recognition using something like a siamese network as you normally would.

# https://github.com/exadel-inc/CompreFace/blob/master/docs/Face-services-and-plugins.md)

I looked at Double Take (UI that lets you do the training of your face recondition easily) and found CompreFace as one of models they support. It looks like what I need but there is a catch... no OpenVino (intel CPUs AI accelerator) support. I really like my low power NVR setup and would like keep it that way. Running AI on CPU without acceleration is both power inefficient and much slower. I have a spare low end GPU but if dump it in the system the current AI acceleration brakes... (I know I can prob fix it but that is a rabbit hole I would prefer to avoid).

This has good UI and also good recognition: https://github.com/exadel-inc/CompreFace

wget -q -O tmp.zip 'https://github.com/exadel-inc/CompreFace/releases/download/v1.1.0/CompreFace_1.1.0.zip' && unzip tmp.zip && rm tmp.zip

https://github.com/exadel-inc/CompreFace/ Is already a good locally-run API for face detection and facial recognition. Thankyou for your suggestion.

If you are looking for open source stuff and are able to self-host it, maybe have a look at CompreFace.

As almost always, on GitHub https://github.com/exadel-inc/CompreFace

Looking into this I found Compreface (https://exadel.com/solutions/compreface/) an open source face recognition software. There are alread some controb scripts, like contrib/photils.lua, who take some images, run them through a tool, then tag them with data coming from the tool. Converting this to use Compreface looks likea promising avenue.