Constructor

obj = cv.BasicFaceRecognizer(ftype)
obj = cv.BasicFaceRecognizer(ftype, 'OptionName',optionValue, ...)

Input

• ftype Face recognizer type. One of:
  • Eigenfaces Face Recognizer based on Eigenfaces.
  • Fisherfaces Face Recognizer based on Fisherfaces.

Options

• NumComponents The number of components, default 0.
  • Eigenfaces The number of components kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient.
  • Fisherfaces The number of components kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically.
• Threshold The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, the prediction returns -1. default realmax

Notes

• Training and prediction must be done on grayscale images, use cv.cvtColor to convert between the color spaces.
• THE EIGENFACES/FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use cv.resize to resize the images.
• This model does not support updating.