cv::face::MACE Class Reference

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) More...

#include <opencv2/face/mace.hpp>

Collaboration diagram for cv::face::MACE:

Public Member Functions
virtual void	salt (const cv::String &passphrase)=0
	optionally encrypt images with random convolution
virtual bool	same (cv::InputArray query) const =0
	correlate query img and threshold to min class value
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
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static cv::Ptr< MACE >	create (int IMGSIZE=64)
	constructor
static cv::Ptr< MACE >	load (const String &filename, const String &objname=String())
	constructor
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

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: [237]

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);

Member Function Documentation

create()

static cv::Ptr< MACE > cv::face::MACE::create ( int  IMGSIZE = 64 )

static

Python:
	cv.face.MACE.create(	[, IMGSIZE]	) ->	retval
	cv.face.MACE_create(	[, IMGSIZE]	) ->	retval

constructor

Parameters

IMGSIZE

images will get resized to this (should be an even number)

load()

static cv::Ptr< MACE > cv::face::MACE::load ( const String &  filename, const String &  objname = String()  )

static

Python:
	cv.face.MACE.load(	filename[, objname]	) ->	retval
	cv.face.MACE_load(	filename[, objname]	) ->	retval

constructor

Parameters

filename	build a new MACE instance from a pre-serialized FileStorage
objname	(optional) top-level node in the FileStorage

salt()

virtual void cv::face::MACE::salt ( const cv::String &  passphrase )

pure virtual

Python:
	cv.face.MACE.salt(	passphrase	) ->	None

optionally encrypt images with random convolution

Parameters

passphrase

a crc64 random seed will get generated from this

same()

virtual bool cv::face::MACE::same ( cv::InputArray  query ) const

pure virtual

Python:
	cv.face.MACE.same(	query	) ->	retval

correlate query img and threshold to min class value

Parameters

query

a Mat with query image

train()

virtual void cv::face::MACE::train ( cv::InputArrayOfArrays  images )

pure virtual

Python:
	cv.face.MACE.train(	images	) ->	None

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

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The documentation for this class was generated from the following file:

• opencv2/face/mace.hpp