deepface VS Face Recognition

You might also want to categorize using either mediapose's holistic model which includes the face mesh or maybe something that gauges emotion like this: https://github.com/serengil/deepface

This is another one I tried but requires programming knowledge. https://github.com/serengil/deepface

It must be a matter of time before we have this, we have so many library's that can detect a face expression. For example: https://github.com/serengil/deepface

I bought a On1 license, but as you point out, it has no facial recognition nor any plans to add it. That also means it has no concept of face regions as implemented in XMP, so that cannot be retrofitted by running face recognition externally, e.g. using an open-source solution like Deepface.

deepface - newer version which has multiple face detectors in it you can mess with, dlib included. Also does face recognition.

If you're looking for models / planning to write some code yourself: Have a look at ArcFace for recognition and RetinaFace for face detection by Insightface. DeepFace repository might also be worth a try.

DeepFace: https://github.com/serengil/deepface

DeepFace utilizes a collection of different face detection models listed here: models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]

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