Face Recognition

Classes
class	cv::datasets::FR_adience
struct	cv::datasets::FR_adienceObj
class	cv::datasets::FR_lfw
struct	cv::datasets::FR_lfwObj

Enumerations
enum	cv::datasets::genderType {   cv::datasets::male = 0,   cv::datasets::female,   cv::datasets::none }

Detailed Description

Adience

Implements loading dataset:

"Adience": http://www.openu.ac.il/home/hassner/Adience/data.html

Usage:

1. From link above download any dataset file: faces.tar.gz\aligned.tar.gz and files with splits: fold_0_data.txt-fold_4_data.txt, fold_frontal_0_data.txt-fold_frontal_4_data.txt. (For face recognition task another splits should be created)
2. Unpack dataset file to some folder and place split files into the same folder.
3. To load data run:

   ./opencv/build/bin/example_datasets_fr_adience -p=/home/user/path_to_created_folder/

Labeled Faces in the Wild

Implements loading dataset:

"Labeled Faces in the Wild": http://vis-www.cs.umass.edu/lfw/

Usage:

1. From link above download any dataset file: lfw.tgz\lfwa.tar.gz\lfw-deepfunneled.tgz\lfw-funneled.tgz and files with pairs: 10 test splits: pairs.txt and developer train split: pairsDevTrain.txt.
2. Unpack dataset file and place pairs.txt and pairsDevTrain.txt in created folder.
3. To load data run:

   ./opencv/build/bin/example_datasets_fr_lfw -p=/home/user/path_to_unpacked_folder/lfw2/

Benchmark

For this dataset was implemented benchmark with accuracy: 0.623833 +- 0.005223 (train split: pairsDevTrain.txt, dataset: lfwa)

To run this benchmark execute:

./opencv/build/bin/example_datasets_fr_lfw_benchmark -p=/home/user/path_to_unpacked_folder/lfw2/

Enumeration Type Documentation

genderType

enum cv::datasets::genderType

#include <opencv2/datasets/fr_adience.hpp>

Enumerator
male  Python: cv.datasets.male
female  Python: cv.datasets.female
none  Python: cv.datasets.none