#include "facerec.hpp"
§ create()
static Ptr<EigenFaceRecognizer> cv::face::EigenFaceRecognizer::create |
( |
int |
num_components = 0 , |
|
|
double |
threshold = DBL_MAX |
|
) |
| |
|
static |
- Parameters
-
num_components | The number of components (read: Eigenfaces) 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. |
threshold | The threshold applied in the prediction. |
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 EigenFaceRecognizer::create.
- threshold see EigenFaceRecognizer::create.
- 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.
The documentation for this class was generated from the following file:
- /build/master-contrib_docs-lin64/opencv_contrib/modules/face/include/opencv2/face/facerec.hpp