openCV的特征脸

Question

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!

Answer 1

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

• http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec_api.html#setting-the-thresholds

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:

• http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec_api.html#facerecognizer-update