#include <opencv2/face/facerec.hpp>
◆ create()
static Ptr< FisherFaceRecognizer > cv::face::FisherFaceRecognizer::create |
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int | num_components = 0, |
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double | threshold = DBL_MAX ) |
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Python: |
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| cv.face.FisherFaceRecognizer.create( | [, num_components[, threshold]] | ) -> | retval |
| cv.face.FisherFaceRecognizer_create( | [, num_components[, threshold]] | ) -> | retval |
- Parameters
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num_components | The number of components (read: Fisherfaces) 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, this method returns -1. |
Notes:
- Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
- THE 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 resize to resize the images.
- This model does not support updating.
Model internal data:
- num_components see FisherFaceRecognizer::create.
- threshold see FisherFaceRecognizer::create.
- eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
- eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
- mean The sample mean calculated from the training data.
- projections The projections of the training data.
- labels The labels corresponding to the projections.
The documentation for this class was generated from the following file: