Skip to content
opencv-wasm

face_EigenFaceRecognizer

faceclassOpenCV 5.0.0
import { face_EigenFaceRecognizer } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

ARGUMENTSConstructor or factory
CLASSface_EigenFaceRecognizer
RETURN TYPEOwned native handle
Call structure. A void return can still write to destination arguments. The parameter descriptions define inputs, outputs and ownership.

Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits face_BasicFaceRecognizer.

face eigen face recognizer value in the OpenCV API.

Upstream declaration ↗

Constructors and members

static create

Create an owned face_EigenFaceRecognizer instance with the supplied configuration.

create(num_components: number, threshold: number): face_EigenFaceRecognizer | null;
3 available overloads
create(): face_EigenFaceRecognizer | null;
create(num_components: number): face_EigenFaceRecognizer | null;
create(num_components: number, threshold: number): face_EigenFaceRecognizer | null;
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.
Returns

The face_EigenFaceRecognizer | null result.

Upstream declaration ↗

clone

Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.

clone(): this;
Returns

The this result.

These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.