Package org.opencv.face

Class 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 Summary

      Constructors 
      Modifier Constructor Description
      protected MACE​(long addr)  

    • Method Summary

      All Methods Static Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      static MACE __fromPtr__​(long addr)  
      static MACE create()
      constructor
      static MACE create​(int IMGSIZE)
      constructor
      protected void finalize()  
      static MACE load​(java.lang.String filename)
      constructor
      static MACE load​(java.lang.String filename, java.lang.String objname)
      constructor
      void salt​(java.lang.String passphrase)
      optionally encrypt images with random convolution
      boolean same​(Mat query)
      correlate query img and threshold to min class value
      void train​(java.util.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

      • Methods inherited from class java.lang.Object

        clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Constructor Detail

      • MACE

        protected MACE​(long addr)

    • Method Detail

      • __fromPtr__

        public static MACE __fromPtr__​(long addr)

      • 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

      • load

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

      • 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

      • salt

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

      • train

        public void train​(java.util.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

      • finalize

        protected void finalize()
                 throws java.lang.Throwable
        Overrides:
        finalize in class Algorithm
        Throws:
        java.lang.Throwable