// This file is part of the OpenCV project.
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
// of this distribution and at http://opencv.org/license.html.
|
|
#ifndef __mace_h_onboard__
|
#define __mace_h_onboard__
|
|
#include "opencv2/core.hpp"
|
|
|
namespace cv {
|
namespace face {
|
|
//! @addtogroup face
|
//! @{
|
|
|
/**
|
@brief 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:
|
@code
|
|
Ptr<face::MACE> mace = face::MACE::create(64);
|
|
vector<Mat> pos_images = ...
|
mace->train(pos_images);
|
|
Mat query = ...
|
bool same = mace->same(query);
|
|
@endcode
|
|
you can also use two-factor authentication, with an additional passphrase:
|
|
@code
|
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);
|
@endcode
|
|
save/load your model:
|
@code
|
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);
|
@endcode
|
|
*/
|
|
class CV_EXPORTS_W MACE : public cv::Algorithm
|
{
|
public:
|
/**
|
@brief optionally encrypt images with random convolution
|
@param passphrase a crc64 random seed will get generated from this
|
*/
|
CV_WRAP virtual void salt(const cv::String &passphrase) = 0;
|
|
/**
|
@brief 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
|
@param images a vector<Mat> with the train images
|
*/
|
CV_WRAP virtual void train(cv::InputArrayOfArrays images) = 0;
|
|
/**
|
@brief correlate query img and threshold to min class value
|
@param query a Mat with query image
|
*/
|
CV_WRAP virtual bool same(cv::InputArray query) const = 0;
|
|
|
/**
|
@brief constructor
|
@param filename build a new MACE instance from a pre-serialized FileStorage
|
@param objname (optional) top-level node in the FileStorage
|
*/
|
CV_WRAP static cv::Ptr<MACE> load(const String &filename, const String &objname=String());
|
|
/**
|
@brief constructor
|
@param IMGSIZE images will get resized to this (should be an even number)
|
*/
|
CV_WRAP static cv::Ptr<MACE> create(int IMGSIZE=64);
|
};
|
|
//! @}
|
|
}/* namespace face */
|
}/* namespace cv */
|
|
#endif // __mace_h_onboard__
|
