Overview
The Face Detection Data Set and Benchmark (FDDB) is a data set of face regions designed for studying the problem of unconstrained face detection. This data set contains the annotations for 5171 faces in a set of 2845 images taken from the Faces in the Wild data set.
Data Annotation
Face Detection Data Set and Benchmark
University of Massachusetts - Amherst
Face annotations
Uncompressing the "FDDB-folds.tgz" file creates a directory "FDDB-folds", which contains files
with names: FDDB-fold-xx.txt
and FDDB-fold-xx-ellipseList.txt
, where xx = {01, 02, ..., 10}
represents the fold-index. Each line in the FDDB-fold-xx.txt
file specifies a path to
an image in the above-mentioned data set. For instance, the entry 2002/07/19/big/img_130
corresponds to originalPics/2002/07/19/big/img_130.jpg
.
The corresponding annotations are included in the file "FDDB-fold-xx-ellipseList.txt" in the following format:
<image name i>
<number of faces in this image =im>
<face i1>
<face i2>
...
<face im>
Here, each face is denoted by: <major_axis_radius minor_axis_radius angle center_x center_y 1>.
Detection output
To be recognized by the evaluation code, the detection output is expected in the following format:
<image name i>
<number of faces in this image =im>
<face i1>
<face i2>
...
<face im>
where the representation of a face depends on the specifics of the shape of the hypothesized image region. The evaluation code supports the following shapes:
-
Rectangular regions Each face region is represented as: <left_x top_y width height detection_score>
-
Elliptical regions Each face region is represented as: <major_axis_radius minor_axis_radius angle center_x center_y detection_score>.
Also, the order of images in the output file is expected to be the same as the order in the file annotatedList.txt.
Citation
Please cite as: Vidit Jain and Erik Learned-Miller. FDDB: A Benchmark for Face Detection in Unconstrained Settings. Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts, Amherst. 2010.
BibTeX entry:
@TechReport{fddbTech,
author = {Vidit Jain and Erik Learned-Miller},
title = {FDDB: A Benchmark for Face Detection in Unconstrained Settings},
institution = {University of Massachusetts, Amherst},
year = {2010},
number = {UM-CS-2010-009}
}