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Query regarding Multiple face recognization system
2 projects | reddit.com/r/MLQuestions | 25 Nov 2021
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.
CompreFace - Free and open-source self-hosted face recognition system from Exadel
5 projects | reddit.com/r/selfhosted | 7 May 2021
As for me, openface is already outdated - the latest release was in 2016. If you look for a library, the easiest to use is ageitgey/face_recognition. The more accurate libraries are davidsandberg/facenet and deepinsight/insightface.
Facial recognition using cluster
1 project | reddit.com/r/RASPBERRY_PI_PROJECTS | 15 Jan 2021
ML training is practically impossible on micro-controllers. Inferencing on the other hand is quite doable, especially if aided by a [TPU coprocessor](https://coral.ai/products/accelerator/). Supposedly with the TPU you can do some quantization-aware training, but I haven't tried this. I am working on a security system that does facial recognition to recognize me and some friends and considers anyone else as an intruder. How I am doing this is by retraining [Facenet](https://github.com/davidsandberg/facenet) with my facial embeddings. Use something like Haar Cascade in OpenCV to get the bounding box for a face and put it through the model to extract face embeddings. You can then save these embeddings as a sort of databases for the faces you want it to recognize during the inferencing phase. After that you can impose something like a SVM classifier to say who in your face database it is. One thing I will note is that the problem is even easier if you are only concerned with one face - in which case it is technically face identification - not recognition. If that is the case, you only need to do a difference calculation between the embeddings you saved during training and the result output from inferencing. If you do end up using the TPU, you can connect to it over USB from inside a container (I only know how to do this in Docker though) too. Hope this was helpful. I am actually looking to use a k8s cluster eventually too as a sort of smart hub for my security system and other devices so I can handle much more traffic (not sure if this is overkill or not on the pi 4s).
Man with foot up on desk in Pelosi's office at Capitol arrested
He might just be a solid techie because the scripts are freely available on github. https://github.com/davidsandberg/facenet
I did a quick google search and found this (Face Recognition using Tensorflow)[https://github.com/davidsandberg/facenet].
Technically anyone can do this, but you will need to know a bit of the tools (and possibly some programming). There's a bunch of models and techniques for image recognition (for example - https://github.com/davidsandberg/facenet)
What are some alternatives?
insightface - State-of-the-art 2D and 3D Face Analysis Project
deepface - A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
CompreFace - Leading free and open-source face recognition system
DeepStack - The World's Leading Cross Platform AI Engine for Edge Devices
Face Recognition - The world's simplest facial recognition api for Python and the command line
anime-face-detector - Anime Face Detector using mmdet and mmpose
Tiny_Faces_in_Tensorflow - A Tensorflow Tiny Face Detector, implementing "Finding Tiny Faces"
DeepCamera - DeepCamera is not only an AI Face Recognition/Person Detection NVR. Machine Learning on the Edge, turn your Camera into AI-powered with Jetson Nano and telegram to protect your privacy.
DeepFake-Detection - Towards deepfake detection that actually works
pytorch2keras - PyTorch to Keras model convertor
MaskTheFace - Convert face dataset to masked dataset