Eigenface algorith can be improved!
This paper suggests that discarding the three most significant principal components, the variation due to lighting is reduced, then Eigenface gives better output. Q1: does somebody can suggest how to do so, or even are we able to modify the Opencv code to remove three components of PCA ? Another suggestion to be able to update the model incrementally, we can apply an incremental PCA algorithm for this purpose.
Q2: is it possible to do so on OpenCV code?
worth trying, but won't be easy. you'll have to come up with a special version of this , that ignores the 1st 3 rows.
Can someone tell me why I get a big distance when calling predict function ?? I initialized the constructor on Test data of the same database (best case) : createEigenFaceRecognizer(30,100); => recognition rate =0 createEigenFaceRecognizer(50,100); => recognition rate =0 createEigenFaceRecognizer(0,100); => recognition rate =0
When I initialize as: createEigenFaceRecognizer(); => recognition rate = is good, but the distance returned by predict reaches up to 5000 !!!! But some true recognitions are returned by a distance equals to about 4000, where some FALSE recognitions by a distance equals to 1500!!!!!! can some one help me to get a reasonable configuration ??