# View Source Evision.Face.EigenFaceRecognizer (Evision v0.1.34)

# Summary

# Types

@type t() :: %Evision.Face.EigenFaceRecognizer{ref: reference()}

Type that represents an `Face.EigenFaceRecognizer`

struct.

**ref**.`reference()`

The underlying erlang resource variable.

# Functions

create

##### Keyword Arguments

**num_components**:`int`

.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**:`double`

.The threshold applied in the prediction.

##### Return

**retval**:`EigenFaceRecognizer`

### 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.

Python prototype (for reference only):

`create([, num_components[, threshold]]) -> retval`

create

##### Keyword Arguments

**num_components**:`int`

.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**:`double`

.The threshold applied in the prediction.

##### Return

**retval**:`EigenFaceRecognizer`

### 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.

Python prototype (for reference only):

`create([, num_components[, threshold]]) -> retval`