We got 80,000+ faces consisting of approximately 20,000 individual people with more faces being added on a daily basis.
All faces are taken by professional photographers as studio portraits or at gala dinners, shows, scenic tours etc. and is therefore not always frontal shots, evenly aligned etc. The faces range from babies to old wrinkly double chinned ones with the majority of people being over 40.
The purpose of the project/application is to find all faces that match each other so a reference can be saved in a db. The facial detection is already done and is not a problem but we struggle a fair bit with getting good consistent results from the facial recognition. Most examples/articles are in the form of access control i.e. where an incoming face is compared against a db of known people and where a person only occurs once in the db. This is different since there are no known people, no source photos to train on etc. Just a big melting pot of faces.
Scenario:
- A picture is taken at an event
- All faces in the picture are detected and saved as individual files
- For each face found the great pot of faces(80,000+) is scanned to find associated faces
- A reference is saved in the db so all faces for a given person can be found later on
We have tried several scenarios but have yet to find one that delivers a consistent prediction. Any good idea how to go about this. Not sure what to train the Recognizer on.
Regards
Rune