Face Recognition based on Eigen-/Fisher-faces

All face recognition models in OpenCV are derived from the abstract base class FaceRecognizer, which provides a unified access to all face recongition algorithms in OpenCV.

Description

Every FaceRecognizer supports the:

• Training of a face recognizer with train on a given set of images (your face database!).
• Prediction of a given sample image, that means a face. The image is given as a matrix.
• Loading/Saving the model state from/to a given XML or YAML.
• Setting/Getting labels info, that is stored as a string. String labels info is useful for keeping names of the recognized people.

Setting the Thresholds

Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is unknown. You can set the thresholds in the constructor. You can also get/set the thresholds at runtime!

Here is an example of setting a threshold for the Eigenfaces method, when creating the model:

% Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0
num_components = 10;
threshold = 10.0;
% Then if you want to have a face recognizer with a confidence threshold,
% create the concrete implementation with the appropiate parameters:
model = cv.BasicFaceRecognizer('Eigenfaces', ...
    'NumComponents',num_components, 'Threshold',threshold);

Sometimes it's impossible to train the model, just to experiment with threshold values. It's possible to set model thresholds during runtime. Let's see how we would set/get the prediction for the Eigenface model, we've created above:

% The following line reads the threshold from the Eigenfaces model:
current_threshold = model.Threshold;
% And this line sets the threshold to 0.0:
model.Threshold = 0.0;

If you've set the threshold to 0.0 as we did above, then:

img = cv.imread('person1/3.jpg', 'Grayscale',true);
% Get a prediction from the model. Note: We've set a threshold of 0.0 above,
% since the distance is almost always larger than 0.0, you'll get -1 as
% label, which indicates, this face is unknown
predicted_label = model.predict(img);
% ...

is going to yield -1 as predicted label, which states this face is unknown.

References

[TP91]:

Turk, M., and Pentland, A. "Eigenfaces for recognition". Journal of Cognitive Neuroscience 3 (1991), 71-86.

[BHK97]:

Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection". IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 711-720.