machine learning - How to adaptively add and use face images collected while authentication to improve performance of face authentication?

Question

My current project is to build a face authentication system. The constraint I have is: during enrollment, the user gives single image for training. However, I can add and use images given by the user while authentication.

The reason I want to add more images into training is, the user environment is not restricted - different lighting conditions, different distance from camera, from different MP cameras. The only relief is the pose is almost frontal.

I think, the above problem is similar to the face tagging app widely available. Can anyone suggest a method to use the available images adaptively and smartly??

--Thanks

Answer 1

To make your classifier robust you need to use condition independent features. For example, you cannot use face color since it depends on lighting conditions and state of a person itself. However, you can use distance between eyes since it is independent of any changes.

I would suggest building some model of such independent features and retrain classifier each time person starts authentication session. Best model I can think of is Active Appearance Model (one of implementations).