Struct opencv::face::MACE

pub struct MACE { /* private fields */ }

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);

Implementations

impl MACE

pub fn load(filename: &str, objname: &str) -> Result<Ptr<MACE>>

constructor

Parameters

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

C++ default parameters

• objname: String()

pub fn create(imgsize: i32) -> Result<Ptr<MACE>>

constructor

Parameters

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

C++ default parameters

• imgsize: 64

Trait Implementations

impl AlgorithmTrait for MACE

fn as_raw_mut_Algorithm(&mut self) -> *mut c_void

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn read(&mut self, fn_: &FileNode) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for MACE

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut FileStorage) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut FileStorage, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl Boxed for MACE

unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Drop for MACE

fn drop(&mut self)

Executes the destructor for this type.

impl From<MACE> for Algorithm

fn from(s: MACE) -> Self

Converts to this type from the input type.

impl MACETrait for MACE

fn as_raw_mut_MACE(&mut self) -> *mut c_void

fn salt(&mut self, passphrase: &str) -> Result<()>

optionally encrypt images with random convolution

fn train(&mut self, images: &impl ToInputArray) -> Result<()>

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

impl MACETraitConst for MACE

fn as_raw_MACE(&self) -> *const c_void

fn same(&self, query: &impl ToInputArray) -> Result<bool>

correlate query img and threshold to min class value

impl Send for MACE