Class cv::face::MACE#

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) View details

#include <opencv2/face/mace.hpp>

Collaboration diagram for cv::face::MACE:

Public Member Functions#

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

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: 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);

Member Function Documentation#

salt()#

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

optionally encrypt images with random convolution

Parameters

passphrase — a crc64 random seed will get generated from this

same()#

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

correlate query img and threshold to min class value

Parameters

query — a Mat with query image

train()#

void cv::face::MACE::train(cv::InputArrayOfArrays 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 with the train images

create()#

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

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() )

constructor

Parameters

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

Source file#

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

opencv2/face/mace.hpp