Face Recognition VS open_model_zoo

Second link is an easy to implement python library is you want to build it yourself https://github.com/ageitgey/face_recognition

It is similar to https://github.com/ageitgey/face_recognition, except that Ageitgey's cli only compares the first face found in the image to the first one found the the second.

face_recognition - fairly easy install as long as dlib compiles for you. Should be able to get your face location ok. This is the older, most mature, but doesn't have as many features and might not have the accuracy of other models. Able to do face recognition.

You could start with DLIB and Geitgey's Face Recognition. They are pretty off-the-shelf packages, which should run fine on a single Intel processor (considering it's not too old).

I tried https://github.com/ageitgey/face_recognition and https://github.com/esimov/pigo so far and they both work OK, but from there, you would need to add EXIF tags into your pictures, so they can be picked up by img-DB.

I want use this library: https://github.com/ageitgey/face_recognition. Its a python library based on https://dlib.net for face recognition

There's quite a few decent python scripts/libraries that would do the job quite simply, such as https://github.com/ageitgey/face_recognition so you could script something up reasonably easily.

So I've been using this Python library to build a simple, Flask based application to recognize a face in an uploaded image from an already encoded set of known faces and it's works pretty well.

As for me, openface is already outdated - the latest release was in 2016.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

Search engines and chatbots are rapidly advancing with the introduction of ChatGPT, but search engines are still running on models like BERT (Bidirectional Encoder Representations from Transformers) for question-answer functionality. An interesting OpenVINO jupyter notebook #213 demonstrating question-answer functionality with a SQuAD v1.1 training set trained BERT model and transformer functions using either an embedded paragraph or a link to a website. I find it still really compelling to see how this works, basically how our widely used search engines function though on a much bigger scale.