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Face Database Research Paper

Face Databases From Other Research Groups

We list some face databases widely used for face related studies, and summarize the specifications of these databases as below.

1. Caltech Occluded Face in the Wild (COFW).  

o       Source: The COFW face dataset is built by

o       Purpose: COFW face dataset contains images with severe facial occlusion. The images are collected from the internet.  

o       Properties:

Properties

Descriptions

# of subjects

-

# of images/videos

1345 images in the training set and 507 images in the testing set.

Static/Videos

Static images.

Single/Multiple faces

Single

Gray/Color

color

Resolution

-

Face pose

Various poses

Facial expression

Various expressions.

Illumination

Various illuminations

3D data

-

Ground truth

29 facial landmark and landmark occlusion annotations

o       Reference: refer to the paper: X. P. Burgos-Artizzu, P. Perona and P. Dollár, "Robust face landmark estimation under occlusion", ICCV 2013, Sydney, Australia, December 2013.

2. Ibug 300 Faces In-the-Wild (ibug 300W) Challenge database.  

o       Source: The ibug 300W face dataset is built by

o       Purpose: The ibug 300W face dataset contains ''in-the-wild'' images collected from the internet.  

o       Properties:

Properties

Descriptions

# of subjects

-

# of images/videos

About 4000+ images.

Static/Videos

Static images.

Single/Multiple faces

Single

Gray/Color

color

Resolution

-

Face pose

Various poses

Facial expression

Various expressions.

Illumination

Various illuminations

3D data

-

Ground truth

68 facial landmark annotations

o       Reference: refer to the paper: C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic, ''300 faces In-the-wild challenge: Database and results'', Image and Vision Computing (IMAVIS), 2016.

3. Ibug 300 Videos in the Wild (ibug 300-VW) Challenge dataset.  

o       Source: The ibug 300VW face dataset is built by

o       Purpose: The ibug 300VW face dataset contains ''in-the-wild'' videos collected from the internet.  

o       Properties:

Properties

Descriptions

# of subjects

-

# of images/videos

114 videos.

Static/Videos

Videos.

Single/Multiple faces

Single

Gray/Color

color

Resolution

-

Face pose

Various poses

Facial expression

Various expressions.

Illumination

Various illuminations

3D data

-

Ground truth

68 facial landmark annotations

o       Reference: refer to the paper: J.Shen, S.Zafeiriou, G. S. Chrysos, J.Kossaifi, G.Tzimiropoulos, and M. Pantic. The first facial landmark tracking in-the-wild challenge: Benchmark and results. In IEEE International Conference on Computer Vision Workshops (ICCVW), 2015.

4. 3D Face Alignment in the Wild (3DFAW) Challenge dataset.  

o       Source: The 3DFAW dataset is built by

o       Purpose: The 3DFAW face dataset contains real and synthetic facial images with 3D facial landmark annotations.  

o       Properties:

Properties

Descriptions

# of subjects

-

# of images/videos

10K+ images.

Static/Videos

Static images.

Single/Multiple faces

Single

Gray/Color

color

Resolution

-

Face pose

Various poses

Facial expression

Various expressions.

Illumination

Various illuminations

3D data

3D facial landmark annotations

Ground truth

66 3D facial landmark annotations

o       Reference: refer to the website: http://mhug.disi.unitn.it/workshop/3dfaw/.

5. Binghamton University facial expression databases.  

o       Source: The Binghamton University facial expression databases are built by

o       Purpose: The Binghamton University facial expression databases record images or videos of subjects with various facial expressions. There are multiple types of subsets. Some subsets contain 4D facial data. Some subsets contain multi-modality facial data.  

o       Properties:

Properties

Descriptions

# of subjects

Number of subjects varies with different data subsets.

# of images/videos

-

Static/Videos

Static and videos.

Single/Multiple faces

Single

Gray/Color

color

Resolution

-

Face pose

-

Facial expression

Various expressions.

Illumination

-

3D data

3D face scann

Ground truth

Facial expression and facial action unit annotations. Some data subsets contain tracked facial landmark locations.

o       Reference: refer to the website: http://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html

6. Gaze Interaction For Everybody (GI4E) dataset.  

o       Source: The GI4E dataset is built by

o       Purpose: The GI4E dataset contains facial videos with continuous head pose annotations.  

o       Properties:

Properties

Descriptions

# of subjects

10

# of images/videos

120 videos.

Static/Videos

Videos.

Single/Multiple faces

Single

Gray/Color

color

Resolution

-

Face pose

Various poses

Facial expression

-

Illumination

-

3D data

-

Ground truth

head pose annotations

o       Reference: refer to the paper: Mikel Ariz, José J. Bengoechea, Arantxa Villanueva, Rafael Cabeza, A novel 2D/3D database with automatic face annotation for head tracking and pose estimation, Computer Vision and Image Understanding, Volume 148, July 2016, Pages 201-210

7. Boston University (BU) head tracking dataset.  

o       Source: The BU head tracking dataset is built by

o       Purpose: The BU head tracking dataset contains facial videos with continuous head pose annotations.  

o       Properties:

Properties

Descriptions

# of subjects

7

# of images/videos

70+ videos

Static/Videos

Videos

Single/Multiple faces

Single

Gray/Color

color

Resolution

320*240

Face pose

Various poses

Facial expression

-

Illumination

Uniform and varying lighting subsets

3D data

-

Ground truth

Continuous head pose annotations

o       Reference: refer to the paper: M. La Cascia, S. Sclaroff, and V. Athitsos, "Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Robust Registration of Texture-Mapped 3D Models", IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 22(4), April, 2000.

8. Acted Facial Expressions in the Wild (AFEW) and Static Facial Expressions in the Wild (SFEW) databases.  

o       Source: The AFEW and SFEW databases are built by

o       Purpose: Acted Facial Expressions In The Wild (AFEW) is a dynamic temporal facial expressions data corpus consisting of close to real world environment extracted from movies. Static Facial Expressions in the Wild (SFEW) has been developed by selecting frames from AFEW.  

o       Properties:

Properties

Descriptions

# of subjects

330

# of images/videos

1426 video sequences in AFEW database. 700 images in SFEW database (SPI category).

Static/Videos

Videos in AFEW, Static images in SFEW.

Single/Multiple faces

Multiple

Gray/Color

color

Resolution

-

Face pose

Various poses

Facial expression

Angry, Disgust, Fear, Happy, Neutral, Sad, Surprise.

Illumination

Various illuminations

3D data

coarse head pose label

Ground truth

5 facial landmark annotations for some images

o       Reference: refer to the paper: Abhinav Dhall, Roland Goecke, Simon Lucey, Tom Gedeon, Collecting Large, "Richly Annotated Facial-Expression Databases from Movies", IEEE Multimedia 2012. Abhinav Dhall, Roland Goecke, Simon Lucey, and Tom Gedeon, "Static Facial Expressions in Tough Conditions: Data, Evaluation Protocol And Benchmark", First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies BeFIT, IEEE International Conference on Computer Vision ICCV2011, Barcelona, Spain, 6-13 November 2011.

9. LFW (Labeled Faces in the Wild) Database  

o       Source: The LFW is built by of , ,

o       Purpose: LFW is a database of face photographs designed for studying the problem of unconstrained face recognition.  Variation in clothing, pose, background, and other variables is large in LFW. 

o       Properties:

Properties

Descriptions

# of subjects

5749

# of images/videos

13,233

Static/Videos

Static

Single/Multiple faces

Single

Gray/Color

color

Resolution

250*250

Face pose

Various poses

Facial expression

Various expressions

Illumination

Various illuminations

3D data

N/A

Ground truth

Identifications of subjects

o       Reference: refer to the paper: Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller, "Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments",
University of , , Technical Report 07-49, October, 2007.

10. Annotated Facial Landmarks in the Wild (AFLW) database  

o       Source: The AFLW is built by

o       Purpose: Annotated Facial Landmarks in the Wild (AFLW) provides a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions.  

o       Properties:

Properties

Descriptions

# of subjects

-

# of images/videos

25,993

Static/Videos

Static

Single/Multiple faces

The Database of Faces

Our Database of Faces, (formerly 'The ORL Database of Faces'), contains a set of face images taken between April 1992 and April 1994 at the lab. The database was used in the context of a face recognition project carried out in collaboration with the Speech, Vision and Robotics Group of the Cambridge University Engineering Department.

There are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). A preview image of the Database of Faces is available.

The files are in PGM format, and can conveniently be viewed on UNIX (TM) systems using the 'xv' program. The size of each image is 92x112 pixels, with 256 grey levels per pixel. The images are organised in 40 directories (one for each subject), which have names of the form , where indicates the subject number (between 1 and 40). In each of these directories, there are ten different images of that subject, which have names of the form , where is the image number for that subject (between 1 and 10).

The database can be retrieved from http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces.tar.Z as a 4.5Mbyte compressed file or from http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces.zip as a ZIP file of similar size.

A convenient reference to the work using the database is the paper Parameterisation of a stochastic model for human face identification. Researchers in this field may also be interested in the author's PhD thesis, Face Recognition Using Hidden Markov Models, available from http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/fsamaria_thesis.ps.Z (~1.7 MB).

When using these images, please give credit to AT&T Laboratories Cambridge.

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