In the industry, that’s known as face (or facial) detection, which is a different problem than face recognition.
Face recognition means computing which individual from some other database of people a particular face belongs to.
There’s also face tracking — detecting a face in an image and then tracking the same face across subsequent images. Which is often implemented by using a face recognition approach, but without any predefined catalog of people — you just dynamically fill up your face database as faces appear in the image sequence / video source.
'Face detection' means it can detect faces. 'Face recognition' means it recognizes the faces. A specific example of the difference: license plate detection will detect the presence of a license plate; license plate recognition will tell you the number on that plate.
In the industry, that’s known as face (or facial) detection, which is a different problem than face recognition.
Face recognition means computing which individual from some other database of people a particular face belongs to.
There’s also face tracking — detecting a face in an image and then tracking the same face across subsequent images. Which is often implemented by using a face recognition approach, but without any predefined catalog of people — you just dynamically fill up your face database as faces appear in the image sequence / video source.
https://yolov8.com/
It detects objects and gives you the bounding box. Then you draw a square on it and add a label.
No fancy LLM needed, just old fashioned machine learning models.
It was detecting faces, not recognizing them.
Recognition implies associating the faces with an ID.
Parent comment is saying the system wasn’t linking the faces to real names, just detecting a face in general.
'Face detection' means it can detect faces. 'Face recognition' means it recognizes the faces. A specific example of the difference: license plate detection will detect the presence of a license plate; license plate recognition will tell you the number on that plate.