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

TRAIN CLASSIFIERS

• 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)

USE CLASSIFIERS

• 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?