115 lines
3.0 KiB
C++
115 lines
3.0 KiB
C++
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// This file is part of the OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef __mace_h_onboard__
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#define __mace_h_onboard__
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#include "opencv2/core.hpp"
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namespace cv {
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namespace face {
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//! @addtogroup face
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//! @{
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/**
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@brief Minimum Average Correlation Energy Filter
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useful for authentication with (cancellable) biometrical features.
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(does not need many positives to train (10-50), and no negatives at all, also robust to noise/salting)
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see also: @cite Savvides04
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this implementation is largely based on: https://code.google.com/archive/p/pam-face-authentication (GSOC 2009)
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use it like:
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@code
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Ptr<face::MACE> mace = face::MACE::create(64);
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vector<Mat> pos_images = ...
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mace->train(pos_images);
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Mat query = ...
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bool same = mace->same(query);
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@endcode
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you can also use two-factor authentication, with an additional passphrase:
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@code
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String owners_passphrase = "ilikehotdogs";
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Ptr<face::MACE> mace = face::MACE::create(64);
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mace->salt(owners_passphrase);
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vector<Mat> pos_images = ...
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mace->train(pos_images);
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// now, users have to give a valid passphrase, along with the image:
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Mat query = ...
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cout << "enter passphrase: ";
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string pass;
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getline(cin, pass);
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mace->salt(pass);
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bool same = mace->same(query);
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@endcode
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save/load your model:
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@code
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Ptr<face::MACE> mace = face::MACE::create(64);
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mace->train(pos_images);
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mace->save("my_mace.xml");
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// later:
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Ptr<MACE> reloaded = MACE::load("my_mace.xml");
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reloaded->same(some_image);
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@endcode
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*/
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class CV_EXPORTS_W MACE : public cv::Algorithm
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{
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public:
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/**
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@brief optionally encrypt images with random convolution
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@param passphrase a crc64 random seed will get generated from this
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*/
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CV_WRAP virtual void salt(const cv::String &passphrase) = 0;
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/**
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@brief train it on positive features
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compute the mace filter: `h = D(-1) * X * (X(+) * D(-1) * X)(-1) * C`
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also calculate a minimal threshold for this class, the smallest self-similarity from the train images
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@param images a vector<Mat> with the train images
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*/
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CV_WRAP virtual void train(cv::InputArrayOfArrays images) = 0;
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/**
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@brief correlate query img and threshold to min class value
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@param query a Mat with query image
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*/
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CV_WRAP virtual bool same(cv::InputArray query) const = 0;
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/**
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@brief constructor
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@param filename build a new MACE instance from a pre-serialized FileStorage
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@param objname (optional) top-level node in the FileStorage
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*/
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CV_WRAP static cv::Ptr<MACE> load(const String &filename, const String &objname=String());
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/**
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@brief constructor
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@param IMGSIZE images will get resized to this (should be an even number)
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*/
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CV_WRAP static cv::Ptr<MACE> create(int IMGSIZE=64);
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};
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//! @}
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}/* namespace face */
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}/* namespace cv */
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#endif // __mace_h_onboard__
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