facenet VS bearid

As for me, openface is already outdated - the latest release was in 2016.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.

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).

He might just be a solid techie because the scripts are freely available on github. https://github.com/davidsandberg/facenet

By the end of my previous post, I had reached a good baseline for the Bearcam Companion app. It was past time to start tracking the code in a version control system. Since we already have the BearID Project on GitHub, I decided to use the same for this project.

We initially focused on building a photo dataset of the bears at Brooks Falls. We collected photos from individuals and a large set from the National Parks Service's bear monitoring program at Katmai. From there we got involved with Dr. Melanie Clapham, a conservation scientist in British Columbia, studying the bears of Glendale Cove. We built a computer to run our ML training and developed an open source application, bearid, which we provided to Melanie as a Docker container she could run in the field on a laptop.

The BearID Project application bearid runs on a server. It takes photos or videos as inputs and outputs a sequence of boxes labeled with the bear's name. This can only happen after someone goes into the field (or, forest), collects a bunch of SD cards from trail cameras, and uploads them to the server. This happens a few times a year. If the trail camera was connected, it could send data to the cloud, but the connection would probably be expensive. If the camera could detect and identify the bear and send only the metadata to a server, we could update the researcher in real time. I have less experience in this area, so connecting it all together with AWS services will be a real learning experience for me.