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Asked: 2026-06-10T20:22:00+00:00

I just recognized some faces with cv2.createEigenFaceRecognizer . But what I want is to

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I just recognized some faces with cv2.createEigenFaceRecognizer.
But what I want is to know how much the input face looks like the calculated eigenfaces.
The idea is that you can rerecognize persons that are not in the database.

EDIT:

for example: I have face A, B and C trained on my model, then I see face C and D. I want to be able to differentiate face C from D.

thank you!

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  1. Editorial Team

    You can find a section on setting thresholds in the documentation on cv::FaceRecognizer at:

    It works just the same for the OpenCV Python Wrapper, which you can easily see when calling help(cv2.createFaceRecognizer) in Python:

    Help on built-in function createEigenFaceRecognizer in module cv2:

createEigenFaceRecognizer(...)
    createEigenFaceRecognizer([, num_components[, threshold]]) -> retval

    So in the code you would create the model with a threshold, I’ll set it to 100.0. Anything below this will yield -1 in the prediction, which means this face is unknown:

    # Create the Eigenfaces model. We are going to use the default
# parameters for this simple example, please read the documentation
# for thresholding:
model = cv2.createEigenFaceRecognizer(threshold=100.0)

    As shown in the demo, you can get the prediction and associated confidence (which is the distance to the nearest neighbor in the your training dataset) with:

    [predicted_label, predicted_confidence] = model.predict(image)

    So if you train your model on the subjects A, B, C AND you are using a threshold, then a prediction for D should yield -1, while A, B or C should be recognized. Given the fact, that you are using a threshold.

    As for adding new faces iteratively without re-estimating the whole model. This is not possible for the Eigenfaces or Fisherfaces method. You always have to call FaceRecognizer::train for these two algorithms to learn the model. The Local Binary Patterns Histograms (LBPH) model, which you can create with cv2.createLBPHFaceRecognizer, supports updating a model without recalculating the other training samples. See the API Documentation on this:

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