face-detection

createEigenFaceRecognizer

C++: Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components=0, double threshold=DBL_MAX)

Parameters:

• num_components – The number of components (read: Eigenfaces) kept for this Prinicpal 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.
• threshold – The threshold applied in the prediciton.

Notes:

• Training and prediction must be done on grayscale images, use cvtColor() to convert between the color spaces.
• THE EIGENFACES 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 resize() to resize the images.
• This model does not support updating.

Model internal data:

• num_components see createEigenFaceRecognizer().
• threshold see createEigenFaceRecognizer().
• eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
• eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
• mean The sample mean calculated from the training data.
• projections The projections of the training data.
• labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.