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I trained a classifier for face detection using opencv_traincascade based on LBP features. When I run cascade.xml on a face, I get many bounding boxes (it is not the best classifier in the world). For generalisability, I make no assumptions on the data and assume the bounding box of maximum size will give me the face region. Sadly, this is not always the case.

Is there any way to solve this problem or do I need a better classifier?

I trained a classifier for face detection using opencv_traincascade based on LBP features. When I run cascade.xml on a face, I get many bounding boxes (it is not the best classifier in the world). For generalisability, I make no assumptions on the data and assume the bounding box of maximum size will give me the face region. Sadly, this is not always the case.

Is there any way to solve this problem or do I need a better classifier?

I trained a classifier for face detection using opencv_traincascade based on LBP features. When I run cascade.xml on a face, I get many bounding boxes (it is not the best classifier in the world). For generalisability, I make no assumptions on the data and assume the bounding box of maximum size will give me the face region. Sadly, this is not always the case.

Is there any way to solve this problem or do I need a better classifier?

EDIT

1. I am training using OpenCV on windows and testing the cascade.xml on Matlab. Are the results affected by doing this? Any compatibility issues?
2. How do people usually test their cascades-on C#, Matlab Matlab, C, C++, Python or using OpenCV itself?