I am looking into score-level fusion in biometrics. Even though I have read quite a lot of papers concerning this subject, I still can't wrap my head around one thing, and that is how the scores of the separate biometrics are combined (which is quite foundational to the fusion subject).
Imagine the situation where I want to fuse face and finger. Then I can assume one of the following:
- I assume that each sample pair is from the same user (subject1_finger_sample & subject1_face_sample)
- I assume that each sample pair can be a fraud as well (subject1_finger_sample & subject2_face_sample)
It seems most papers that I read assume the first, although they do not explain why. An example is given here. This means the following would be a valid test for the fusion algorithm:
(subject1_finger_sample + subject1_face_sample)
(subject2_finger_sample + subject2_face_sample)
But the following would not be:
(subject1_finger_sample + subject2_face_sample)
(subject2_finger_sample + subject1_face_sample)
Because they seem to assume that BOTH samples in a two-modal biometric setup will always be from the same person. However, in real life this seems counterintuitive: two impostors can easily work together, the first tries to forge my fingerprint, and the second moves his face in front of the webcam..
Any scientific reasoning for this?