facenet

Face recognition using Tensorflow (by davidsandberg)

Stats

Basic facenet repo stats
0
11,684
0.0
2 months ago

davidsandberg/facenet is an open source project licensed under MIT License which is an OSI approved license.

Facenet Alternatives

Similar projects and alternatives to facenet based on common topics and language
  • GitHub repo deepface

    A Lightweight Deep Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Framework for Python

  • GitHub repo CompreFace

    Free and open-source face recognition system from Exadel

  • GitHub repo DeepCamera

    Open source face recognition on Raspberry Pi. SharpAI is open source stack for machine learning engineering with private deployment and AutoML for edge computing. DeepCamera is application of SharpAI designed for connecting computer vision model to surveillance camera. Developers can run same code on Raspberry Pi/Android/PC/AWS to boost your AI production development.

  • GitHub repo Face Recognition

    The world's simplest facial recognition api for Python and the command line

  • GitHub repo Tiny_Faces_in_Tensorflow

    A Tensorflow Tiny Face Detector, implementing "Finding Tiny Faces"

  • GitHub repo DeepStack

    The World's Leading Cross Platform AI Engine for Edge Devices

  • GitHub repo szabadfogasu-maszk

    A face mask detection system using Tensorflow/Keras and OpenCV, for the "<19 Szabadfogású Számítógép" competition in 2020.

NOTE: The number of mentions on this list indicates mentions on common posts. Hence, a higher number means a better facenet alternative or higher similarity.

Posts

Posts where facenet has been mentioned. We have used some of these posts to build our list of alternatives and similar projects - the last one was on 2021-01-08.
  • Facial recognition using cluster
    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
    reddit.com/r/politics | 2021-01-08
    He might just be a solid techie because the scripts are freely available on github. https://github.com/davidsandberg/facenet
    reddit.com/r/politics | 2021-01-08
    I did a quick google search and found this (Face Recognition using Tensorflow)[https://github.com/davidsandberg/facenet].
    reddit.com/r/politics | 2021-01-08
    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)