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YouTube Faces DB
2D Box
Classification
Face
|...
License: Unknown

Overview

Welcome to YouTube Faces Database, a database of face videos designed for studying the
problem of unconstrained face recognition in videos.
The data set contains 3,425 videos
of 1,595 different people. All the videos were downloaded from YouTube.
An average of 2.15 videos are available for each subject. The shortest clip duration is 48
frames, the longest clip is 6,070 frames, and the average length of a video clip is 181.3 frames.

Number of videos per person:

#videos 1 2 3 4 5 6
#people 591 471 307 167 51 8

In designing our video data set and benchmarks we follow the example of the 'Labeled Faces
in the Wild' LFW image collection. Specifically, our goal
is to produce a large scale collection of videos along with labels indicating the identities
of a person appearing in each video.
In addition, we publish benchmark tests, intended to
measure the performance of video pair-matching techniques on these videos.
Finally, we provide
descriptor encodings for the faces appearing in these videos, using well established descriptor
methods.

Data Collection

Collection setup:
We begin by using the 5,749 names of subjects included in the LFW
data set to search YouTube for videos of these same individuals.
The top six results for each query were downloaded.
We minimize the number of duplicate videos
by considering two videos' names with edit distance less than 3 to be duplicates.
Downloaded
videos are then split to frames at 24fps. We detect faces in these videos using the Viola-Jones
face detector. Automatic screening was performed to eliminate detections of less than 48 consecutive
frames, where detections were considered consecutive if the Euclidean distance between their
detected centers was less than 10 pixels. This process ensures that the videos contain stable
detections and are long enough to provide useful information for the various recognition algorithms.
Finally, the remaining videos were manually verified to ensure that (a) the videos are correctly
labeled by subject, (b) are not semi-static, still-image slide-shows, and (c) no identical
videos are included in the database.

Data Annotation

For each person in the database there is a file called subject_name.labeled_faces.txt
The data in this file is in the following format:
filename,[ignore],x,y,width,height,[ignore],[ignore]
where:
x,y are the center of the face and the width and height are of the rectangle that the face is in.
For example:
$ head -3 Richard_Gere.labeled_faces.txt
Richard_Gere\3\3.618.jpg,0,262,165,132,132,0.0,1
Richard_Gere\3\3.619.jpg,0,260,164,131,131,0.0,1
Richard_Gere\3\3.620.jpg,0,261,165,129,129,0.0,1

Citation

If you use this database, or refer to its results, please cite the following paper:

@INPROCEEDINGS{5995566,
author={L. {Wolf} and T. {Hassner} and I. {Maoz}},
booktitle={CVPR 2011},
title={Face recognition in unconstrained videos with matched background similarity}, year={2011},
volume={}, number={}, pages={529-534},
}
Data Summary
Type
Video,
Amount
620.952K
Size
49GB
Provided by
Lior Wolf
A faculty member at the School of Computer Science at Tel Aviv University and a research scientist at Facebook AI Research.
| Amount 620.952K | Size 49GB
YouTube Faces DB
2D Box Classification
Face
License: Unknown

Overview

Welcome to YouTube Faces Database, a database of face videos designed for studying the
problem of unconstrained face recognition in videos.
The data set contains 3,425 videos
of 1,595 different people. All the videos were downloaded from YouTube.
An average of 2.15 videos are available for each subject. The shortest clip duration is 48
frames, the longest clip is 6,070 frames, and the average length of a video clip is 181.3 frames.

Number of videos per person:

#videos 1 2 3 4 5 6
#people 591 471 307 167 51 8

In designing our video data set and benchmarks we follow the example of the 'Labeled Faces
in the Wild' LFW image collection. Specifically, our goal
is to produce a large scale collection of videos along with labels indicating the identities
of a person appearing in each video.
In addition, we publish benchmark tests, intended to
measure the performance of video pair-matching techniques on these videos.
Finally, we provide
descriptor encodings for the faces appearing in these videos, using well established descriptor
methods.

Data Collection

Collection setup:
We begin by using the 5,749 names of subjects included in the LFW
data set to search YouTube for videos of these same individuals.
The top six results for each query were downloaded.
We minimize the number of duplicate videos
by considering two videos' names with edit distance less than 3 to be duplicates.
Downloaded
videos are then split to frames at 24fps. We detect faces in these videos using the Viola-Jones
face detector. Automatic screening was performed to eliminate detections of less than 48 consecutive
frames, where detections were considered consecutive if the Euclidean distance between their
detected centers was less than 10 pixels. This process ensures that the videos contain stable
detections and are long enough to provide useful information for the various recognition algorithms.
Finally, the remaining videos were manually verified to ensure that (a) the videos are correctly
labeled by subject, (b) are not semi-static, still-image slide-shows, and (c) no identical
videos are included in the database.

Data Annotation

For each person in the database there is a file called subject_name.labeled_faces.txt
The data in this file is in the following format:
filename,[ignore],x,y,width,height,[ignore],[ignore]
where:
x,y are the center of the face and the width and height are of the rectangle that the face is in.
For example:
$ head -3 Richard_Gere.labeled_faces.txt
Richard_Gere\3\3.618.jpg,0,262,165,132,132,0.0,1
Richard_Gere\3\3.619.jpg,0,260,164,131,131,0.0,1
Richard_Gere\3\3.620.jpg,0,261,165,129,129,0.0,1

Citation

If you use this database, or refer to its results, please cite the following paper:

@INPROCEEDINGS{5995566,
author={L. {Wolf} and T. {Hassner} and I. {Maoz}},
booktitle={CVPR 2011},
title={Face recognition in unconstrained videos with matched background similarity}, year={2011},
volume={}, number={}, pages={529-534},
}
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