Awesome

Multi-view face detection trained on AFLW dataset.

This repo has the auxiliary Matlab and C++ code in order to replicate the face detection experiments in our paper. If you use this code for your own research, you must reference our journal paper:

BAdaCost: Multi-class Boosting with Costs. Antonio Fernández-Baldera, José M. Buenaposada, and Luis Baumela. Pattern Recognition, Elsevier. In press, 2018. DOI:10.1016/j.patcog.2018.02.022

Requirements

Clone toolbox.badacost.public repo, with our modified version of Piotr Dollar toolbox with the BAdaCost algorithm with cost-sensitive trees. Go to its directory and execute Matlab. Then from Matlab prompt, execute addpath(createpath(PATH_TO_TOOLBOX)) and then toolboxCompile.
Clone toolbox.badacost.faces.public, a set of tools and code to perform the faces detection experiments with the AFLW, AFW, PASCAL faces and FDDB datasets.
We train our detector with face images from the Annotated Facial Landmarks in the Wild dataset. Thus, from the AFLW site you have to download:
- aflw-images-0.tar.gz,
- aflw-images-2.tar.gz and
- aflw-images-3.tar.gz
by decompressing the tar.gz files we will get the following directory structure:
```
  aflw
  `-- data
  	`-- flickr
  	   |-- 0
  	   |-- 2
  	   `-- 3
  
```
The path to the flicker folder in the example, with the AFLW training images (dirs 0, 2, 3) will be refered as AFLW_PATH from now on.
The negative face images are obtained from the PASCAL VOC 2007 dataset. Therefore we need to dowload the following files from the PASCAL VOC site:
- VOCtest_06-Nov-2007.tar
- VOCtestnoimgs_06-Nov-2007.tar
- VOCtrainval_06-Nov-2007.tar
by decompressing the tar files we will get the following directory structure:
```
    pascal_voc
  	`-- 2007
  	    `-- VOCdevkit
  		`-- VOC2007
  		   |-- Annotations
  		   `-- JPEGImages
```
The path to the pascal_voc/2007/VOCdevkit/VOC2007 folder in the example, with the JPEGImages and Annotations subdirs will be refered as PASCAL_VOC2007_PATH from now on.
The PASCAL Faces dataset, is extracted from different face images in the PASCAL VOC datasets. Therefore we need to dowload the following files from the PASCAL VOC site:
- To be uncompressed in directory pascal_voc/2007:
  - VOCtrainval_06-Nov-2007.tar
  - VOCdevkit_08-Jun-2007.tar
  - VOCtest_06-Nov-2007.tar
  - VOCtestnoimgs_06-Nov-2007.tar
- To be uncompressed in directory pascal_voc/2008:
  - VOCtrainval_14-Jul-2008.tar
  - VOCdevkit_14-Apr-2008.tar
  - VOC2008_test.tar
- To be uncompressed in directory pascal_voc/2009_test:
  - InWGD4LN.tar
- To be uncompressed in directory pascal_voc/2010_test:
  - VOC2010_test.tar
- Untar the following files in the directory PASCAL_VOC/2011_test and then move 2011_test/Test/VOCdevkit to 2011_test/VOCdevkit
  - VO2011_test.tar
- Untar the following files in the directory PASCAL_VOC/2012:
  - VOCdevkit_18-May-2011.tar
  - VOCtrainval_11-May-2012.tar
by decompressing the tar files we will get the following directory structure:
```
   pascal_voc
  |
  |-- 2007
  |   `-- VOCdevkit
  |       `-- VOC2007
  |          |-- Annotations
  |          `-- JPEGImages
  |-- 2008_test
  |   `-- VOCdevkit
  |       `-- VOC2008
  |           |-- Annotations
  |           `-- JPEGImages
  |-- 2009_test
  |   `-- VOCdevkit
  |       `-- VOC2009
  |           |-- Annotations
  |           `-- JPEGImages
  |-- 2010_test
  |   `-- VOCdevkit
  |       `-- VOC2010
  |           |-- Annotations
  |           `-- JPEGImages
  |-- 2011_test
  |   `-- VOCdevkit
  |       `-- VOC2011
  |           |-- Annotations
  |           `-- JPEGImages
  `-- 2012
      `-- VOCdevkit
  	`-- VOC2012
  	    |-- Annotations
  	    `-- JPEGImages
```
The path to the pascal_voc folder in the example, with the JPEGImages and Annotations subdirs will be refered as PASCAL_VOC_PATH from now on.
The AFW dataset, which is in the file AFW.zip, can be downloaded from the AFW site. Decompress all the images in the .zip in a single directory and se the variable AFW_PATH in the main.m script to this directory.
Download the FDDB dataset and prepare data for P.Dollar toolbox by running the script:

fddb2PiotrFormatMulticlass.m