人脸识别-常用的数据库Face Databases From Other Research Groups

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 California Institute of Technology,

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. Doll�r, "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 the Intelligent Behavior Understanding Group (ibug) at Imperial College London,

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 the Intelligent Behavior Understanding Group (ibug) at Imperial College London,

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 Organizers of the 3DFAW challenge,

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 Dr. Lijun Yin at Binghamton University and other collaborators,

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 GI4E group,

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, Jos� 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 the image and video computing group at Boston University,

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 Australian National University, University of Canberra, and Commonwealth Scientific and Industrial Research Organisation, Australia ,

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 University of Massachusetts, Amherst,

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 Massachusetts, Amherst, Technical Report 07-49, October, 2007.

 

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

o       Source: The AFLW is built by Graz University of Technology ,

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	Multiple
Gray/Color	color
Resolution	High resolution
Face pose	Various poses
Facial expression	Various expressions
Illumination	Various illuminations
3D data	coarse head pose estimation
Ground truth	21 point markup

o       Reference: refer to the paper: Martin Koestinger, Paul Wohlhart, Peter M. Roth, and Horst Bischof, "Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization", In First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies, 2011.

 

11. Labeled Face Parts in the Wild (LFPW) Dataset  

o       Source: The LFPW database is built by Kriegman-Belhumeur Vision Technologies, LLC.

o       Purpose: LFPW was used to evaluate a face part (facial fiducial point) detection method. Release 1 of LFPW consists of 1,432 faces from images downloaded from the web using simple text queries on sites such as google.com, flickr.com, and yahoo.com. Each image was labeled by three MTURK workers, and 29 fiducial points are included in dataset.  

o       Properties:

Properties	Descriptions
# of subjects	-
# of images/videos	1432
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	color
Resolution	-
Face pose	Various poses
Facial expression	Various expressions
Illumination	Various illuminations
3D data	N/A
Ground truth	Annotated 29 fiducial points

o       Reference: refer to the paper : Peter N. Belhumeur, David W. Jacobs, David J. Kriegman, and Neeraj Kumar, �Localizing Parts of Faces Using a Consensus of Exemplars,� Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Additional annotations can be found here: "http://ibug.doc.ic.ac.uk/resources".

 

12. Helen dataset  

o       Source: The Helen database is built by The Image Formation & Processing (IFP) Group at the University of Illinois, people from Adobe Systems Inc. and Facebook Inc.

o       Purpose: Helen database provides a large-scale collection of annotated facial 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	2000 training and 330 testing
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	color
Resolution	High resolution
Face pose	Various poses
Facial expression	Various expressions
Illumination	Various illuminations
3D data	N/A
Ground truth	Annotated 194 facial landmarks

o       Reference: refer to the paper:Vuong Le, Jonathan Brandt, Zhe Lin, Lubomir Boudev, and Thomas S. Huang, �Interactive Facial Feature Localization�, ECCV2012. Additional annotations can be found here: "http://ibug.doc.ic.ac.uk/resources".

 

 

13. The Facial Recognition Technology (FERET) Database

o       Source: the FERET database is sponsored by the Defense Advanced Research Products Agency (DARPA).

o       Purpose: the FERET database is widely used as the standard face database to evaluate the face recognition systems. It may also be used for face pose estimation and eye detection.

o       Properties:

Properties	Descriptions
# of subjects	1199
# of images/videos	14051
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	eight-bit gray
Resolution	256*384
Face pose	7 categories: Frontal, quarter-left, quarter-right, half-left, half-right, full-left, full-right
Facial expression	Slight facial expression changes
Illumination	Controlled illumination
3D data	N/A
Ground truth	Positions of eyes, nose, and mouth Identifications of subjects

o       Reference: refer to the paper �P. J. Phillips, Hyeonjoon Moon, S. A. Rizvi, and P. J. Rauss, The FERET evaluation methodology for face recognition algorithm, IEEE Trans. on PAMI, vol. 22, no. 10, pp. 1090-1104, October 2000� and the online document �http://www.itl.nist.gov/iad/humanid/feret/feret_master.html�.

 

14. Face Recognition Grand Challenge (FRGC) Database

o       Source: the FRGC database is jointly sponsored by several government agencies interested in improving the capabilities of face recognition technology.

o       Purpose: the primary goal of the FRGC database is to evaluate face recognition technology. It may also be used for eye detection.

o       Properties:

Properties	Descriptions
# of subjects	222 (large still training set) 466 (validation set)
# of images/videos	12,776 (large still training set) 943 *8 (3D training set) 4007 *8 (validation set)
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Color
Resolution	1704*2272 or 1200*1600
Face pose	Frontal view
Facial expression	Neutral and smiling
Illumination	Controlled and uncontrolled illumination
3D data	Yes (range and texture)
Ground truth	Positions of eyes, nose, and mouth Identifications of subjects

o       Reference: Please refer to the paper �P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, Overview of the face recognition grand challenge, Proc. of CVPR05, no. 1, pp. 947�954, June 2005� and the original document under the directory �BEE_DIST\doc�.

 

15. CAS-PEAL Face Database

o       Source: CAS-PEAL database is obtained from Chinese Academy of Science.

o       Purpose: CAS-PEAL database is used to evaluate the face recognition systems. It may also be used for eye detection, face pose estimation, and facial expression recognition.

o       Properties:

Properties	Descriptions
# of subjects	1040 (595 males and 445 females) of Asians
# of images/videos	30,900
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	eight-bit gray
Resolution	360*480
Face pose	21 pose angles vertical: up, middle, and down horizontal: left to right (67�, 45�, 22�, 0�, -22�, -45�, -67�)
Facial expression	6 facial expressions: neutral, eye closing, frown, smile, surprise,  and mouth open
Illumination	15 lighting conditions
Accessories	3 kinds of glasses and 3 kinds of caps
3D data	N/A
Ground truth	Positions of eyes Identifications of subjects Face pose angles Facial expression labels Illumination positions

o       Reference: Please refer to the technical report JDL-TR-04-FR-001 �The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations�.

 

16. CMU Pose, Illumination, and Expression (PIE) Database

o       Source: The PIE database is obtained from the Robotics Institute of Carnegie Mellon University.

o       Purpose: PIE database is used to evaluate the face recognition systems. It may also be used for facial feature detection, face pose estimation, and facial expression recognition.

o       Properties:

Properties	Descriptions
# of subjects	68
# of images/videos	41,368
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Color
Resolution	640*486
Face pose	13 pose angles in vertical and horizontal
Facial expression	4 facial expressions: neutral, eye closing, smiling, and talking
Illumination	N/A
Accessories	Glasses
3D data	N/A
Ground truth	Some feature point data Identifications of subjects Measured locations of camera Head pose Facial expression labels Illumination positions
Additional materials	Background images

o       Reference: Please refer to the CMU Technical Report CMU-RI-TR-01-02 �The CMU Pose, Illumination, and Expression (PIE) Database of Human Faces�.

 

17. CMU Face Database (Frontal and Profile)

o       Source: this database is obtained from the Robotics Institute of Carnegie Mellon University. It combines images collected at CMU and MIT.

o       Purpose: this database is primarily used for face detection task. It may also be used for eye detection and facial feature detection.

o       Properties:

Properties	Descriptions
# of subjects	N/A
# of images/videos	169 frontal-view face images 202 profile face images
Static/Videos	Static
Single/Multiple faces	Multiple
Gray/Color	eight-bit gray
Resolution	N/A
Face pose	Frontal and profile
Facial expression	Various facial expressions
Illumination	Various lighting conditions
Accessories	Various
3D data	N/A
Ground truth	Positions of eyes, nose tip, mouth corners, and mouth center for each face (frontal-view face); Positions of eye corner, eye, nose, nose tip, mouth corner, mouth center, chin, earlobe, and ear tip for each face (profile face)

 

18. Yale Face Database

o       Source: this database is constructed by Yale University.

o       Purpose: this database can be used for face recognition and facial expression recognition.

o       Properties:

Properties	Descriptions
# of subjects	15
# of images/videos	165
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	eight-bit gray
Resolution	320*243
Face pose	Frontal view
Facial expression	6 facial expressions: neutral, happiness, sadness, sleepiness, surprise, and wink
Illumination	3 lighting conditions: center-light, left-light, and right-light
Accessories	Glasses
3D data	N/A
Ground truth	Identifications of subjects Facial expression labels Illumination positions

o       Reference: Please refer to the paper �P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. on PAMI, vol. 19, no. 7, pp. 711-720, July 1997�.

 

19. Yale Face Database B

o       Source: this database is constructed by Yale University.

o       Purpose: this database can be used for face recognition, face pose estimation, and eye detection.

o       Properties:

Properties	Descriptions
# of subjects	10
# of images/videos	5760
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Gray
Resolution	640*480 (eye distance ~ 90pixels)
Face pose	9 poses
Facial expression	Neutral
Illumination	64 lighting conditions and 1 ambient illumination
Accessories	N/A
3D data	N/A
Ground truth	Identifications of subjects Face pose Illumination positions Coordinates of eyes and mouth (frontal view) Coordinates of face center (other views)

o        Reference: Please refer to the paper �A. S. Georghiades and P. N. Belhumeur, From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose, IEEE Trans. on. PAMI, vol.23, no.6, pp.643-660, June 2001�.

 

20. Georgia Tech Face Database

o       Source: this database is constructed by Georgia Institute of Technology.

o       Purpose: this database is primarily used for face recognition. It may also be used for face detection.

o       Properties:

Properties	Descriptions
# of subjects	50
# of images/videos	750
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	color
Resolution	640*480
Face pose	Nearly frontal-view or quarter-profile images
Facial expression	Various
Illumination	Various
Accessories	Glasses
3D data	N/A
Ground truth	Identifications of subjects Coordinates of left-upper corner and right-bottom corner of face rectangle

 

21. AR_Face_Database

o       Source: this database is constructed by Aleix Martinez and Robert Benavente in the Computer Vision Center (CVC) at the UAB.

o       Purpose: this database is primarily used for face recognition. It may also be used for facial expression recognition.

o       Properties:

Properties	Descriptions
# of subjects	126 (70 male and 56 female)
# of images/videos	4000
Static/Videos	Static images and image sequences
Single/Multiple faces	Single
Gray/Color	color
Resolution	768*576
Face pose	Nearly frontal-view or quarter-profile images
Facial expression	4 facial expressions: neutral, smile, anger, and scream
Illumination	3 illumination conditions: left, right, and all side lights on
Accessories	Sun glasses, scarf
3D data	N/A
Ground truth	Identifications of subjects Facial expression labels

o       Reference: Please refer to the technical report �A. M. Martinez and R. Benavente, The AR Face Database, CVC Technical Report #24, June 1998�.

 

22. UMIST_Face_Database

o       Source: this database was constructed by the University of Manchester Institute of Science and Technology that merged with the Victoria University of Manchester to form the University of Manchester.

o       Purpose: this database is primarily used for face recognition.

o       Properties:

Properties	Descriptions
# of subjects	20
# of images/videos	564
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Eight-bit gray
Resolution	92*112
Face pose	From profile to frontal views
Facial expression	neutral
Illumination	N/A
Accessories	Glasses
3D data	N/A
Ground truth	Cropped face region Identifications of subjects

o       Reference: Please refer to the paper �Daniel B Graham and Nigel M Allinson, Characterizing Virtual Eigensignatures for General Purpose Face Recognition, Face Recognition: From Theory to Applications, NATO ASI Series F, Computer and Systems Sciences, vol. 163, 
H. Wechsler, P. J. Phillips, V. Bruce, F. Fogelman-Soulie and T. S. Huang (eds), pp. 446-456, 1998�.

 

23. ORL Database of Faces

o       Source: this database is constructed by AT&T Laboratories Cambridge.

o       Purpose: this database is primarily used for face recognition.

o       Properties:

Properties	Descriptions
# of subjects	40
# of images/videos	400
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Eight-bit gray
Resolution	92*112
Face pose	Moderate pose variation (up and down, quarter-profile to frontal-view)
Facial expression	3 facial expressions: neutral, smiling, closed eye
Illumination	N/A
Accessories	Glasses
3D data	N/A
Ground truth	Cropped face region Identifications of subjects

o       Reference: Please refer to the paper �F. S. Samaria and A. C. Harter �Parameterisation of a stochastic model for human face identification�, Proc. of 2^nd IEEE workshop on Applications of Computer Vision, pp. 138-142, 1994�.

 

24. MIT CBCL Face Database #1

o       Source: this database is constructed by Center for Biological and Computational Learning at MIT.

o       Purpose: this database is primarily used for face detection.

o       Properties:

Properties	Descriptions
# of subjects	N/A
# of images/videos	Training set: 2,429 faces and 4,548 non-faces Test set: 472 faces and 23,573 non-faces.
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Eight-bit gray
Resolution	19*19
Face pose	Moderate pose variation
Facial expression	Moderate facial expression changes
Illumination	Moderate illumination variation
Accessories	Glasses
3D data	N/A
Ground truth	Cropped face region

 

25. Pointing Head Pose Image Database

o       Source: this database is obtained from http://www-prima.inrialpes.fr/Pointing04/data-face.html .

o       Purpose: this database is primarily used for face pose estimation task. It may also be used for face recognition.

o       Properties:

Properties	Descriptions
# of subjects	15
# of images/videos	2790
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Color
Resolution	384*288
Face pose	Vertical: -90, -60, -30, -15, 0, +15, +30, +60 +90 Horizontal: -90, -75, -60, -45, -30, -15, 0, +15, +30, +45, +60, +75, +90
Facial expression	Neutral
Illumination	N/A
Accessories	Glasses
3D data	N/A
Ground truth	Identifications of subjects Face pose angles

o       Reference: Please refer to the paper �N. Gourier, D. Hall, and J. L. Crowley, �Estimating Face Orientation from Robust Detection of Salient Facial Features,� Proc. of Pointing 2004, ICPR, International Workshop on Visual Observation of Deictic Gestures�.

 

26. Spacetime Face Database

o       Source: this database is constructed by the University of Washington Graphics and Imaging Laboratory.

o       Purpose: this database is primarily used for face modeling and animation.

o       Properties: 384 face meshes, each with about 23K vertices.

o       Reference: Please refer to the paper �Li Zhang, Noah Snavely, Brian Curless, and Steve Seitz, �Spacetime Faces: High-resolution capture for modeling and animation,� Proc. of ACM SIGGRAPH2004�.

 

27. BioID Database

o       Source: this database is constructed by HumanScan company.

o       Purpose: this database can be used for face detection, face recognition and eye detection.

o       Properties:

Properties	Descriptions
# of subjects	23
# of images/videos	1521
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Gray
Resolution	382*288 (eye distance ~ 50pixels)
Face pose	Frontal
Facial expression	Various
Illumination	Various lighting conditions
Accessories	Various
3D data	N/A
Ground truth	Coordinates of eyes. Coordinates of 20 feature points (Eyebrow corners, eye, mouth and tip of chin)

�        Reference: Please refer to the paper �O. Jesorsky, K. Kirchberg, R. Frischholz, Robust Face Detection Using the Hausdorff Distance, Audio and Video based Person Authentication - AVBPA 2001, pages 90-95. Springer, 2001.�.

28. CVL Face Database

o       Source: this database is constructed by Peter Peer, Computer Vision Laboratory of the University of Ljubljana.

o       Purpose: this database can be used for face detection, feature detection, face recognition and 3D face modeling.

o       Properties:

Properties	Descriptions
# of subjects	114
# of images/videos	798
Static/Videos	Static
Single/Multiple faces	Single
Gray/Color	Color
Resolution	640*480
Face pose	Horizontal: -90, -45, 0, 45, 90
Facial expression	3 facial expressions: serious, smiling( showing teeth and showing no teeth)
Illumination	N/A
Accessories	N/A
3D data	N/A
Ground truth	N/A

�        Reference: Please contact Peter Peer ([email protected]) and Computer Vision Laboratory of the University of Ljubljana for the database.

29. NIST Mugshot Identification Database (MID)

o       Source: this database is constructed by the National  Institue of Standards and Technology.

o    Purpose: this database is primarily for use in development and testing of automated mugshot identification systems.

o       Properties:

Properties	Descriptions
# of subjects	1573 (1495 male and  78 female)
# of images/videos	3248
Static/Videos	Static
Single/Multiple faces	Single?
Gray/Color	Gray
Resolution	Varying in size from 1" - 21/2" in height
Face pose	front and profile
Facial expression	N/A
Illumination	N/A
Accessories	N/A
3D data	N/A
Ground truth	Subject identity

30. The University of Oulu Physics-Based Face Database

o       Source: this database is collected at the Machine Vision and Media Processing Unit, University of Oulu.

o       Purpose: this database can be used for research in face recognition and color.

o       Properties:

Properties	Descriptions
# of subjects	125
# of images/videos	N/A (each person has 16 frontal views and an additional 16 if the person has glasses)
Static/Video	Static
Single/Multiple faces	Single
Gray/Color	Color
Resolution	428*569
Face pose	Minor various poses
Facial expression	N/A
Illumination	Four illuminants: Horizon, Incandescent, Fluorescent and Daylight.
Accessories	Glasses
3D data	N/A
Ground truth	N/A

�        Reference: Please refer to the papsers "A physics-based face database for color research, Journal of Electronic Imaging Vol. 9 No. 1 pp.  32-38." and "Color correction of face images under different illuminants by RGB eigenfaces,  Proc. 2nd Audio- and Video-Based Biometric Person Authentication Conference (AVBPA99), March 22-23, Washington DC USA pp. 148-153."

31. UCD Color Face Image (UCFI) Database

o       Source: this database is constructed by the Department of Electronic & Electrical Engineering, University College Dublin. The images are required form a wide variety of sources such as digital cameras, pictures scanned using photo-scanner, other face databases and the world wide web.

o       Purpose: this database is primarily to test new face detection algorithms using color information.

o       Properties:

Properties	Descriptions
# of subjects	N/A
# of images/videos	299
Static/Videos	Static
Single/Multiple faces	Single?
Gray/Color	Color
Resolution	Vaious
Face pose	Frontal, profile, intermediate, upright and rotated
Facial expression	Various
Illumination	Various
Accessories	Glasses, sunglasses, beards, moustaches
3D data	N/A
Ground truth	Hand segmantation