How do I reduce the effects of lighting conditions for a face expression classification system?

asked 2013-12-22 07:24:36 -0600

_Robert gravatar image

I have started making a system of telling what expression a given face has. This is my method:


  • Find and crop the face rectangle.
  • Capture sample face rectangles with different facial expressions (Happy, sad, neutral)
  • Train several cascade classifiers using opencv_traincascade currently using:

    -numStages 1 
    -stageType BOOST 
    -featureType HAAR 
    -w 42 
    -h 53 
    -bt GAB 
    -minHitRate 0.99 
    -maxFalseAlarmRate 0.009 
    -weightTrimRate 0.95 
    -maxDepth 1 
    -maxWeakCount 100

    (I don't totally understand all of these parameters)


  • Find and crop the face rectangle.
  • Run the face over each cascade classifier.
  • Pick the classifier with the highest result.

So far it has worked quite well, however, it is a little sensitive to lighting conditions.

I want add some kind of filter after before training and classification to reduce the effects of lighting conditions, but I am not sure what ones will work best:

  • Gabor?
  • Laplace?
  • Canny edge?
  • Gaussian blur?
  • Equalise histogram? etc...
  • Use LBP?

What filters might improve the results? or are there any other way too improve my face recognition system?

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