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)
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virtual void | salt (const cv::String &passphrase)=0 |
| optionally encrypt images with random convolution
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virtual bool | same (cv::InputArray query) const =0 |
| correlate query img and threshold to min class value
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virtual void | train (cv::InputArrayOfArrays images)=0 |
| 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
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| Algorithm () |
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virtual | ~Algorithm () |
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virtual void | clear () |
| Clears the algorithm state.
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virtual bool | empty () const |
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
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virtual String | getDefaultName () const |
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virtual void | read (const FileNode &fn) |
| Reads algorithm parameters from a file storage.
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virtual void | save (const String &filename) const |
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void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
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virtual void | write (FileStorage &fs) const |
| Stores algorithm parameters in a file storage.
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void | write (FileStorage &fs, const String &name) const |
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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: [236]
this implementation is largely based on: https://code.google.com/archive/p/pam-face-authentication (GSOC 2009)
use it like:
vector<Mat> pos_images = ...
mace->train(pos_images);
bool
same = mace->same(query);
n-dimensional dense array class
Definition mat.hpp:828
static cv::Ptr< MACE > create(int IMGSIZE=64)
constructor
virtual bool same(cv::InputArray query) const =0
correlate query img and threshold to min class value
std::shared_ptr< _Tp > Ptr
Definition cvstd_wrapper.hpp:23
you can also use two-factor authentication, with an additional passphrase:
String owners_passphrase =
"ilikehotdogs";
mace->salt(owners_passphrase);
vector<Mat> pos_images = ...
mace->train(pos_images);
cout << "enter passphrase: ";
string pass;
getline(cin, pass);
mace->salt(pass);
bool same = mace->same(query);
std::string String
Definition cvstd.hpp:151
save/load your model:
mace->train(pos_images);
mace->save("my_mace.xml");
reloaded->same(some_image);
static cv::Ptr< MACE > load(const String &filename, const String &objname=String())
constructor