Class MACE

java.lang.Object
org.opencv.core.Algorithm
org.opencv.face.MACE
public class MACE extends Algorithm
Minimum Average Correlation Energy Filter useful for authentication with (cancellable) biometrical features. (does not need many positives to train (10-50), and no negatives at all, also robust to noise/salting) see also: CITE: Savvides04 this implementation is largely based on: https://code.google.com/archive/p/pam-face-authentication (GSOC 2009) use it like: Ptr<face::MACE> mace = face::MACE::create(64); vector<Mat> pos_images = ... mace->train(pos_images); Mat query = ... bool same = mace->same(query); you can also use two-factor authentication, with an additional passphrase: String owners_passphrase = "ilikehotdogs"; Ptr<face::MACE> mace = face::MACE::create(64); mace->salt(owners_passphrase); vector<Mat> pos_images = ... mace->train(pos_images); // now, users have to give a valid passphrase, along with the image: Mat query = ... cout << "enter passphrase: "; string pass; getline(cin, pass); mace->salt(pass); bool same = mace->same(query); save/load your model: Ptr<face::MACE> mace = face::MACE::create(64); mace->train(pos_images); mace->save("my_mace.xml"); // later: Ptr<MACE> reloaded = MACE::load("my_mace.xml"); reloaded->same(some_image);

  • Constructor Details

    • MACE

      protected MACE(long addr)

  • Method Details

    • __fromPtr__

      public static MACE __fromPtr__(long addr)

    • salt

      public void salt(String passphrase)
      optionally encrypt images with random convolution
      Parameters:
      passphrase - a crc64 random seed will get generated from this

    • train

      public void train(List<Mat> images)
      train it on positive features compute the mace filter: h = D(-1) * X * (X(+) * D(-1) * X)(-1) * C also calculate a minimal threshold for this class, the smallest self-similarity from the train images
      Parameters:
      images - a vector<Mat> with the train images

    • same

      public boolean same(Mat query)
      correlate query img and threshold to min class value
      Parameters:
      query - a Mat with query image
      Returns:
      automatically generated

    • load

      public static MACE load(String filename, String objname)
      constructor
      Parameters:
      filename - build a new MACE instance from a pre-serialized FileStorage
      objname - (optional) top-level node in the FileStorage
      Returns:
      automatically generated

    • load

      public static MACE load(String filename)
      constructor
      Parameters:
      filename - build a new MACE instance from a pre-serialized FileStorage
      Returns:
      automatically generated

    • create

      public static MACE create(int IMGSIZE)
      constructor
      Parameters:
      IMGSIZE - images will get resized to this (should be an even number)
      Returns:
      automatically generated

    • create

      public static MACE create()
      constructor
      Returns:
      automatically generated