| OpenCV 3.4.3 Open Source Computer Vision

Classes | Enumerations
Face Recognition
Framework for working with different datasets

## 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

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