I am toying with the various FaceRecognition algorithms, and I'd like to better understand the confidence values so that I can have a sense of when to ignore a match or when I can rely on a match.
Using the ATT face database, I did test 1 where I trained on the 40 faces then ran prediction on a known face (With unknown image of course). I then did a second test where I trained on 39 faces and ran prediction on an unknown face (happens to be the same image as used in test 1).
The values I got were: Eigenspace - 1806 when face known - 2618 when face unknown
Fisherface - 372 known - 841 unknown
LBPH - 36 known - 55 unknown.
If I am interpreting the algorithms correctly, Eigenspace and Fisherface work in a high dimension space and try to find the closest neighbor for a given test image. This means the confidence value will change depending on data set, and I can not have a simple threshold. Is there any other information I can gather, such as average distance between clusters, so that I can understand if I should keep or ignore a prediction?
In regards to LBPH, is this confidence acting the same way?
Many thanks