pigo VS Face Recognition

Then I saw this library https://github.com/esimov/pigo but as far as I know, this library only detects if an image has face in it, but cannot really classify the face. CMIIW.

The task consists of porting a small portion of the PIGO face detection GO library (https://github.com/esimov/pigo/commit/29b25278c3e3436416440404b95cd3c18e145b3b) especially the face landmarks extraction section to WebAssembly/JavaScript or plain C depending of the programming language your are proficient with.

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'll use this post / opportunity to ask the HN community

(I know this is a technically complicated, and potentially sensitive subject)

I've been approached by a few people who are trying to make prescription glasses less expensive. It's a mix of (licensed) opticians, people interested in offering community health services, etc.

What is the current state of the art for PD measurement based on face / pupil tracking / detection using laptop webcams or mobile (front) cameras?

I imagine (obviously?) that there must be scholar research on the acceptable error margins for a PD measurement (depending on the type of vision condition, i.e. farsightedness, etc.)?

Would using something like Handsfree or https://github.com/esimov/pigo (which has pupil detection) be a good start, or would these be an ~order of magnitude off in terms of the necessary margins?

Thanks a lot.

Camera connected to a PI? Something like this could run locally: https://github.com/ageitgey/face_recognition

Have you tried this repo: github

One of the most common challenges is the black-box problem, when the pipeline becomes too complex to understand it would happen. This can make it difficult to identify issues with the system or to understand why it isn't working as we expected or make accurate predictions that saiwa company find out the solution for Face Recognition. Another challenge is the time required for organizations to deploy a machine learning model, which is increasing and make real-time computing difficult . To overcome these challenges, it's important to have an efficient and rigorous ML pipeline . ML level 0 involves a manual process with its own set of challenges, while ML level 1 involves ML pipeline automation and additional components . A well-defined machine learning pipeline can help to abstract the complex process into a series of steps, allowing each team to work independently on specific tasks such as data collection, data preparation, model training, model evaluation, and model deployment.

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.

You'd have alot more luck with something like DLIB or an open source implementation such as: https://github.com/ageitgey/face_recognition

To know more about face_recognition module https://github.com/ageitgey/face_recognition