Face Analysis

Detailed Description

• Face module changelog
• Face Recognition with OpenCV

Classes
class	cv::face::BasicFaceRecognizer
struct	cv::face::CParams
class	cv::face::EigenFaceRecognizer
class	cv::face::Facemark
	Abstract base class for all facemark models. More...
class	cv::face::FacemarkAAM
class	cv::face::FacemarkLBF
class	cv::face::FacemarkTrain
	Abstract base class for trainable facemark models. More...
class	cv::face::FaceRecognizer
	Abstract base class for all face recognition models. More...
class	cv::face::FisherFaceRecognizer
class	cv::face::LBPHFaceRecognizer
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) More...
class	cv::face::PredictCollector
	Abstract base class for all strategies of prediction result handling. More...
class	cv::face::StandardCollector
	Default predict collector. More...

Typedefs
typedef bool(*	cv::face::FN_FaceDetector) (InputArray, OutputArray, void *userData)

Functions
Ptr< Facemark >	cv::face::createFacemarkAAM ()
	construct an AAM facemark detector
Ptr< Facemark >	cv::face::createFacemarkKazemi ()
	construct a Kazemi facemark detector
Ptr< Facemark >	cv::face::createFacemarkLBF ()
	construct an LBF facemark detector
void	cv::face::drawFacemarks (InputOutputArray image, InputArray points, Scalar color=Scalar(255, 0, 0))
	Utility to draw the detected facial landmark points.
bool	cv::face::getFaces (InputArray image, OutputArray faces, CParams *params)
	Default face detector This function is mainly utilized by the implementation of a Facemark Algorithm. End users are advised to use function Facemark::getFaces which can be manually defined and circumvented to the algorithm by Facemark::setFaceDetector.
bool	cv::face::getFacesHAAR (InputArray image, OutputArray faces, const String &face_cascade_name)
bool	cv::face::loadDatasetList (String imageList, String annotationList, std::vector< String > &images, std::vector< String > &annotations)
	A utility to load list of paths to training image and annotation file.
bool	cv::face::loadFacePoints (String filename, OutputArray points, float offset=0.0f)
	A utility to load facial landmark information from a given file.
bool	cv::face::loadTrainingData (std::vector< String > filename, std::vector< std::vector< Point2f > > &trainlandmarks, std::vector< String > &trainimages)
	This function extracts the data for training from .txt files which contains the corresponding image name and landmarks. The first file in each file should give the path of the image whose landmarks are being described in the file. Then in the subsequent lines there should be coordinates of the landmarks in the image i.e each line should be of the form x,y where x represents the x coordinate of the landmark and y represents the y coordinate of the landmark.
bool	cv::face::loadTrainingData (String filename, std::vector< String > &images, OutputArray facePoints, char delim=' ', float offset=0.0f)
	A utility to load facial landmark dataset from a single file.
bool	cv::face::loadTrainingData (String imageList, String groundTruth, std::vector< String > &images, OutputArray facePoints, float offset=0.0f)
	A utility to load facial landmark information from the dataset.

Typedef Documentation

FN_FaceDetector

typedef bool(* cv::face::FN_FaceDetector) (InputArray, OutputArray, void *userData)

#include <opencv2/face/facemark_train.hpp>

Function Documentation

createFacemarkAAM()

Ptr< Facemark > cv::face::createFacemarkAAM

(

)

Python:
	cv.face.createFacemarkAAM(		) ->	retval

#include <opencv2/face/facemark.hpp>

construct an AAM facemark detector

createFacemarkKazemi()

Ptr< Facemark > cv::face::createFacemarkKazemi

(

)

Python:
	cv.face.createFacemarkKazemi(		) ->	retval

#include <opencv2/face/facemark.hpp>

construct a Kazemi facemark detector

createFacemarkLBF()

Ptr< Facemark > cv::face::createFacemarkLBF

(

)

Python:
	cv.face.createFacemarkLBF(		) ->	retval

#include <opencv2/face/facemark.hpp>

construct an LBF facemark detector

drawFacemarks()

void cv::face::drawFacemarks	(	InputOutputArray	image,
		InputArray	points,
		Scalar	color = Scalar(255, 0, 0)
	)

Python:
	cv.face.drawFacemarks(	image, points[, color]	) ->	image

#include <opencv2/face/facemark_train.hpp>

Utility to draw the detected facial landmark points.

Parameters

image	The input image to be processed.
points	Contains the data of points which will be drawn.
color	The color of points in BGR format represented by cv::Scalar.

Example of usage

std::vector<Rect> faces;
std::vector<std::vector<Point2f> > landmarks;
facemark->getFaces(img, faces);
facemark->fit(img, faces, landmarks);
for(int j=0;j<rects.size();j++){
    face::drawFacemarks(frame, landmarks[j], Scalar(0,0,255));
}

getFaces()

bool cv::face::getFaces	(	InputArray	image,
		OutputArray	faces,
		CParams *	params
	)

#include <opencv2/face/facemark_train.hpp>

Default face detector This function is mainly utilized by the implementation of a Facemark Algorithm. End users are advised to use function Facemark::getFaces which can be manually defined and circumvented to the algorithm by Facemark::setFaceDetector.

Parameters

image	The input image to be processed.
faces	Output of the function which represent region of interest of the detected faces. Each face is stored in cv::Rect container.
params	detector parameters

Example of usage

std::vector<cv::Rect> faces;
CParams params("haarcascade_frontalface_alt.xml");
cv::face::getFaces(frame, faces, &params);
for(int j=0;j<faces.size();j++){
    cv::rectangle(frame, faces[j], cv::Scalar(255,0,255));
}
cv::imshow("detection", frame);

getFacesHAAR()

bool cv::face::getFacesHAAR	(	InputArray	image,
		OutputArray	faces,
		const String &	face_cascade_name
	)

Python:
	cv.face.getFacesHAAR(	image, face_cascade_name[, faces]	) ->	retval, faces

#include <opencv2/face/facemark_train.hpp>

loadDatasetList()

bool cv::face::loadDatasetList	(	String	imageList,
		String	annotationList,
		std::vector< String > &	images,
		std::vector< String > &	annotations
	)

Python:
	cv.face.loadDatasetList(	imageList, annotationList, images, annotations	) ->	retval

#include <opencv2/face/facemark_train.hpp>

A utility to load list of paths to training image and annotation file.

Parameters

imageList	The specified file contains paths to the training images.
annotationList	The specified file contains paths to the training annotations.
images	The loaded paths of training images.
annotations	The loaded paths of annotation files.

Example of usage:

String imageFiles = "images_path.txt";
String ptsFiles = "annotations_path.txt";
std::vector<String> images_train;
std::vector<String> landmarks_train;
loadDatasetList(imageFiles,ptsFiles,images_train,landmarks_train);

loadFacePoints()

bool cv::face::loadFacePoints	(	String	filename,
		OutputArray	points,
		float	offset = 0.0f
	)

Python:
	cv.face.loadFacePoints(	filename[, points[, offset]]	) ->	retval, points

#include <opencv2/face/facemark_train.hpp>

A utility to load facial landmark information from a given file.

Parameters

filename	The filename of file contains the facial landmarks data.
points	The loaded facial landmark points.
offset	An offset value to adjust the loaded points.

Example of usage

std::vector<Point2f> points;
face::loadFacePoints("filename.txt", points, 0.0f);

The annotation file should follow the default format which is

version: 1
n_points:  68
{
212.716603 499.771793
230.232816 566.290071
...
}

where n_points is the number of points considered and each point is represented as its position in x and y.

loadTrainingData() [1/3]

bool cv::face::loadTrainingData	(	std::vector< String >	filename,
		std::vector< std::vector< Point2f > > &	trainlandmarks,
		std::vector< String > &	trainimages
	)

Python:
	cv.face.loadTrainingData(	filename, images[, facePoints[, delim[, offset]]]	) ->	retval, facePoints
	cv.face.loadTrainingData(	imageList, groundTruth, images[, facePoints[, offset]]	) ->	retval, facePoints
	cv.face.loadTrainingData(	filename, trainlandmarks, trainimages	) ->	retval

#include <opencv2/face/facemark_train.hpp>

This function extracts the data for training from .txt files which contains the corresponding image name and landmarks. The first file in each file should give the path of the image whose landmarks are being described in the file. Then in the subsequent lines there should be coordinates of the landmarks in the image i.e each line should be of the form x,y where x represents the x coordinate of the landmark and y represents the y coordinate of the landmark.

For reference you can see the files as provided in the HELEN dataset

Parameters

filename	A vector of type cv::String containing name of the .txt files.
trainlandmarks	A vector of type cv::Point2f that would store shape or landmarks of all images.
trainimages	A vector of type cv::String which stores the name of images whose landmarks are tracked

Returns

A boolean value. It returns true when it reads the data successfully and false otherwise

loadTrainingData() [2/3]

bool cv::face::loadTrainingData	(	String	filename,
		std::vector< String > &	images,
		OutputArray	facePoints,
		char	delim = ' ',
		float	offset = 0.0f
	)

Python:
	cv.face.loadTrainingData(	filename, images[, facePoints[, delim[, offset]]]	) ->	retval, facePoints
	cv.face.loadTrainingData(	imageList, groundTruth, images[, facePoints[, offset]]	) ->	retval, facePoints
	cv.face.loadTrainingData(	filename, trainlandmarks, trainimages	) ->	retval

#include <opencv2/face/facemark_train.hpp>

A utility to load facial landmark dataset from a single file.

Parameters

filename	The filename of a file that contains the dataset information. Each line contains the filename of an image followed by pairs of x and y values of facial landmarks points separated by a space. Example /home/user/ibug/image_003_1.jpg 336.820955 240.864510 334.238298 260.922709 335.266918 ... /home/user/ibug/image_005_1.jpg 376.158428 230.845712 376.736984 254.924635 383.265403 ...
images	A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
facePoints	The loaded landmark points for all training data.
delim	Delimiter between each element, the default value is a whitespace.
offset	An offset value to adjust the loaded points.

Example of usage

cv::String imageFiles = "../data/images_train.txt";
cv::String ptsFiles = "../data/points_train.txt";
std::vector<String> images;
std::vector<std::vector<Point2f> > facePoints;
loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);

loadTrainingData() [3/3]

bool cv::face::loadTrainingData	(	String	imageList,
		String	groundTruth,
		std::vector< String > &	images,
		OutputArray	facePoints,
		float	offset = 0.0f
	)

Python:
	cv.face.loadTrainingData(	filename, images[, facePoints[, delim[, offset]]]	) ->	retval, facePoints
	cv.face.loadTrainingData(	imageList, groundTruth, images[, facePoints[, offset]]	) ->	retval, facePoints
	cv.face.loadTrainingData(	filename, trainlandmarks, trainimages	) ->	retval

#include <opencv2/face/facemark_train.hpp>

A utility to load facial landmark information from the dataset.

Parameters

imageList	A file contains the list of image filenames in the training dataset.
groundTruth	A file contains the list of filenames where the landmarks points information are stored. The content in each file should follow the standard format (see face::loadFacePoints).
images	A vector where each element represent the filename of image in the dataset. Images are not loaded by default to save the memory.
facePoints	The loaded landmark points for all training data.
offset	An offset value to adjust the loaded points.

Example of usage

cv::String imageFiles = "../data/images_train.txt";
cv::String ptsFiles = "../data/points_train.txt";
std::vector<String> images;
std::vector<std::vector<Point2f> > facePoints;
loadTrainingData(imageFiles, ptsFiles, images, facePoints, 0.0f);

example of content in the images_train.txt

/home/user/ibug/image_003_1.jpg
/home/user/ibug/image_004_1.jpg
/home/user/ibug/image_005_1.jpg
/home/user/ibug/image_006.jpg

example of content in the points_train.txt

/home/user/ibug/image_003_1.pts
/home/user/ibug/image_004_1.pts
/home/user/ibug/image_005_1.pts
/home/user/ibug/image_006.pts